Final Analyses
load(here::here("data.rdata"))library(tidyverse)
library(lme4)
library(nlme)
library(psych)
library(finalfit)
library(naniar)
library(recipes)
library(stringr)
library(reshape2)
library(sjPlot)
library(emmeans)
here::here()DESCRIPTIVE STATISTICS
Tables
nlsy %>%
group_by(race, sex) %>%
count() %>%
knitr::kable()| race | sex | n |
|---|---|---|
| white | MALE | 1560 |
| white | FEMALE | 1538 |
| latinx | MALE | 178 |
| latinx | FEMALE | 141 |
| black | MALE | 1541 |
| black | FEMALE | 1535 |
| other | MALE | 429 |
| other | FEMALE | 357 |
add %>%
group_by(race, sex) %>%
count() %>%
knitr::kable()| race | sex | n |
|---|---|---|
| white | male | 1477 |
| white | female | 1675 |
| latinx | male | 244 |
| latinx | female | 286 |
| black | male | 516 |
| black | female | 683 |
| other | male | 103 |
| other | female | 108 |
nlsy %>%
ggplot() +
geom_bar(aes(race, fill = sex),
position = "dodge") +
theme(legend.title = element_blank()) +
labs(
x = "Race",
y = "N"
)
add %>%
ggplot() +
geom_bar(aes(race, fill = sex),
position = "dodge") +
theme(legend.title = element_blank()) +
labs(
x = "Race",
y = "N"
)
##### NLSY
proportion <- nlsy %>%
group_by(sex, ever_suspended) %>%
count() %>%
na.omit()
proportion <- spread(proportion, key = ever_suspended, value = n) %>%
tibble(.)
proportion$total <- proportion$No + proportion$Yes
proportion$prop <- round(proportion$Yes/proportion$total, 2)
colnames(proportion) <- c("Sex", "Not Suspended", "Suspended", "N", "Proportion")
NLSY_proportion <- proportion
knitr::kable(NLSY_proportion)| Sex | Not Suspended | Suspended | N | Proportion |
|---|---|---|---|---|
| MALE | 2724 | 984 | 3708 | 0.27 |
| FEMALE | 3083 | 488 | 3571 | 0.14 |
####### ADD HEALTH
proportion <- add %>%
group_by(sex, ever_suspended) %>%
count() %>%
na.omit()
proportion <- spread(proportion, key = ever_suspended, value = n) %>%
tibble(.)
proportion$total <- proportion$no + proportion$yes
proportion$prop <- round(proportion$yes/proportion$total, 2)
colnames(proportion) <- c("Sex", "Not Suspended", "Suspended", "N", "Proportion")
ADD_proportion <- proportion
knitr::kable(ADD_proportion)| Sex | Not Suspended | Suspended | N | Proportion |
|---|---|---|---|---|
| male | 1485 | 855 | 2340 | 0.37 |
| female | 2234 | 518 | 2752 | 0.19 |
NLSY_proportion %>%
ggplot() +
geom_col(aes(Sex, (Proportion * 100)),
position = "dodge") +
labs(title = "Proportion of Students Suspended by Gender",
subtitle = "NLSY",
caption = "If bigger bar, disproportiate % of that group are suspended",
x = "Race",
y = "Percent of students") +
theme(legend.position = "none") 
ADD_proportion %>%
ggplot() +
geom_col(aes(Sex, (Proportion * 100)),
position = "dodge") +
labs(title = "Proportion of Students Suspended by Race and Gender",
subtitle = "ADD Health",
caption = "If bigger bar, disproportiate % of that group are suspended",
x = "Race",
y = "Percent of students") +
theme(legend.position = "none") 
##### NLSY
proportion <- nlsy %>%
group_by(race, sex, ever_suspended) %>%
count() %>%
na.omit()
proportion <- spread(proportion, key = ever_suspended, value = n) %>%
tibble(.)
proportion$total <- proportion$No + proportion$Yes
proportion$prop <- round(proportion$Yes/proportion$total, 2)
colnames(proportion) <- c("Race", "Sex", "Not Suspended", "Suspended", "N", "Proportion")
NLSY_proportion <- proportion
knitr::kable(NLSY_proportion)| Race | Sex | Not Suspended | Suspended | N | Proportion |
|---|---|---|---|---|---|
| white | MALE | 1280 | 280 | 1560 | 0.18 |
| white | FEMALE | 1399 | 139 | 1538 | 0.09 |
| latinx | MALE | 133 | 45 | 178 | 0.25 |
| latinx | FEMALE | 123 | 18 | 141 | 0.13 |
| black | MALE | 988 | 553 | 1541 | 0.36 |
| black | FEMALE | 1235 | 300 | 1535 | 0.20 |
| other | MALE | 323 | 106 | 429 | 0.25 |
| other | FEMALE | 326 | 31 | 357 | 0.09 |
####### ADD HEALTH
proportion <- add %>%
group_by(race, sex, ever_suspended) %>%
count() %>%
na.omit()
proportion <- spread(proportion, key = ever_suspended, value = n) %>%
tibble(.)
proportion$total <- proportion$no + proportion$yes
proportion$prop <- round(proportion$yes/proportion$total, 2)
colnames(proportion) <- c("Race", "Sex", "Not Suspended", "Suspended", "N", "Proportion")
ADD_proportion <- proportion
knitr::kable(ADD_proportion)| Race | Sex | Not Suspended | Suspended | N | Proportion |
|---|---|---|---|---|---|
| white | male | 1014 | 463 | 1477 | 0.31 |
| white | female | 1453 | 222 | 1675 | 0.13 |
| latinx | male | 146 | 98 | 244 | 0.40 |
| latinx | female | 227 | 59 | 286 | 0.21 |
| black | male | 249 | 267 | 516 | 0.52 |
| black | female | 462 | 221 | 683 | 0.32 |
| other | male | 76 | 27 | 103 | 0.26 |
| other | female | 92 | 16 | 108 | 0.15 |
NLSY_proportion %>%
ggplot() +
geom_col(aes(Race, (Proportion * 100), fill = Sex),
position = "dodge") +
labs(title = "Proportion of Students Suspended by Race and Gender",
subtitle = "NLSY",
caption = "If bigger bar, disproportiate % of that group are suspended",
x = "Race",
y = "Percent of students") +
theme(legend.position = "none") 
ADD_proportion %>%
ggplot() +
geom_col(aes(Race, (Proportion * 100), fill = Sex),
position = "dodge") +
labs(title = "Proportion of Students Suspended by Race and Gender",
subtitle = "ADD Health",
caption = "If bigger bar, disproportiate % of that group are suspended",
x = "Race",
y = "Percent of students") +
theme(legend.position = "none") 
Inferential Statistics
Race X Gender Effects
nlsy_race_sex_model <- glm(ever_suspended ~ race * sex,
family = binomial, data = nlsy)
summary(nlsy_race_sex_model)Call: glm(formula = ever_suspended ~ race * sex, family = binomial, data = nlsy)
Deviance Residuals: Min 1Q Median 3Q Max
-0.9429 -0.6595 -0.6290 -0.4353 2.2108
Coefficients: Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.51983 0.06597 -23.036 < 2e-16 racelatinx 0.43614 0.18464 2.362 0.0182
raceblack 0.93950 0.08469 11.093 < 2e-16 raceother 0.40561 0.12993 3.122 0.0018 sexFEMALE -0.78921 0.11072 -7.128 1.02e-12 racelatinx:sexFEMALE -0.04891 0.32509 -0.150 0.8804
raceblack:sexFEMALE -0.04551 0.13864 -0.328 0.7427
raceother:sexFEMALE -0.44948 0.24514 -1.834 0.0667 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7329.5 on 7278 degrees of freedomResidual deviance: 6929.5 on 7271 degrees of freedom AIC: 6945.5
Number of Fisher Scoring iterations: 4
plot_model(nlsy_race_sex_model, type = "pred", terms = c("race", "sex"))
nlsy_suspend_fig <- emmeans(nlsy_race_sex_model, specs = pairwise ~ race | sex,
type = "response", adjust = "none")
nlsy_suspend_fig$emmeans sex = MALE: race prob SE df asymp.LCL asymp.UCL white 0.1795 0.00972 Inf 0.1612 0.199 latinx 0.2528 0.03258 Inf 0.1944 0.322 black 0.3589 0.01222 Inf 0.3353 0.383 other 0.2471 0.02082 Inf 0.2086 0.290
sex = FEMALE: race prob SE df asymp.LCL asymp.UCL white 0.0904 0.00731 Inf 0.0770 0.106 latinx 0.1277 0.02810 Inf 0.0819 0.194 black 0.1954 0.01012 Inf 0.1764 0.216 other 0.0868 0.01490 Inf 0.0617 0.121
Confidence level used: 0.95 Intervals are back-transformed from the logit scale
$contrasts sex = MALE: contrast odds.ratio SE df null z.ratio p.value white / latinx 0.647 0.1194 Inf 1 -2.362 0.0182 white / black 0.391 0.0331 Inf 1 -11.093 <.0001 white / other 0.667 0.0866 Inf 1 -3.122 0.0018 latinx / black 0.604 0.1091 Inf 1 -2.790 0.0053 latinx / other 1.031 0.2120 Inf 1 0.148 0.8820 black / other 1.706 0.2113 Inf 1 4.309 <.0001
sex = FEMALE: contrast odds.ratio SE df null z.ratio p.value white / latinx 0.679 0.1817 Inf 1 -1.447 0.1478 white / black 0.409 0.0449 Inf 1 -8.145 <.0001 white / other 1.045 0.2172 Inf 1 0.211 0.8328 latinx / black 0.602 0.1569 Inf 1 -1.946 0.0517 latinx / other 1.539 0.4842 Inf 1 1.370 0.1706 black / other 2.555 0.5074 Inf 1 4.722 <.0001
Tests are performed on the log odds ratio scale
add_race_sex_model <- glm(ever_suspended ~ race * sex,
family = binomial, data = add)
summary(add_race_sex_model)Call: glm(formula = ever_suspended ~ race * sex, family = binomial, data = add)
Deviance Residuals: Min 1Q Median 3Q Max
-1.2072 -0.8673 -0.5333 1.1479 2.0104
Coefficients: Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.78393 0.05609 -13.976 < 2e-16 racelatinx 0.38529 0.14212 2.711 0.00671 raceblack 0.85373 0.10444 8.174 2.97e-16 raceother -0.25097 0.23096 -1.087 0.27720
sexfemale -1.09478 0.09132 -11.989 < 2e-16 * racelatinx:sexfemale 0.14600 0.21621 0.675 0.49949
raceblack:sexfemale 0.28758 0.15096 1.905 0.05678 .
raceother:sexfemale 0.38047 0.36318 1.048 0.29482
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 5936.2 on 5091 degrees of freedomResidual deviance: 5551.1 on 5084 degrees of freedom AIC: 5567.1
Number of Fisher Scoring iterations: 4
plot_model(add_race_sex_model, type = "pred", terms = c("race", "sex"))
add_suspend_fig <- emmeans(add_race_sex_model, specs = pairwise ~ race | sex,
type = "response", adjust = "none")
add_suspend_fig$emmeans sex = male: race prob SE df asymp.LCL asymp.UCL white 0.313 0.01207 Inf 0.2903 0.338 latinx 0.402 0.03138 Inf 0.3420 0.464 black 0.517 0.02200 Inf 0.4743 0.560 other 0.262 0.04333 Inf 0.1863 0.355
sex = female: race prob SE df asymp.LCL asymp.UCL white 0.133 0.00828 Inf 0.1171 0.150 latinx 0.206 0.02393 Inf 0.1633 0.257 black 0.324 0.01790 Inf 0.2895 0.360 other 0.148 0.03418 Inf 0.0928 0.228
Confidence level used: 0.95 Intervals are back-transformed from the logit scale
$contrasts sex = male: contrast odds.ratio SE df null z.ratio p.value white / latinx 0.680 0.0967 Inf 1 -2.711 0.0067 white / black 0.426 0.0445 Inf 1 -8.174 <.0001 white / other 1.285 0.2968 Inf 1 1.087 0.2772 latinx / black 0.626 0.0986 Inf 1 -2.974 0.0029 latinx / other 1.889 0.4900 Inf 1 2.454 0.0141 black / other 3.018 0.7266 Inf 1 4.589 <.0001
sex = female: contrast odds.ratio SE df null z.ratio p.value white / latinx 0.588 0.0958 Inf 1 -3.261 0.0011 white / black 0.319 0.0348 Inf 1 -10.470 <.0001 white / other 0.879 0.2462 Inf 1 -0.462 0.6440 latinx / black 0.543 0.0910 Inf 1 -3.643 0.0003 latinx / other 1.494 0.4600 Inf 1 1.305 0.1917 black / other 2.751 0.7783 Inf 1 3.576 0.0003
Tests are performed on the log odds ratio scale
This is a reproduction of prior analytic work that shows yes, there are large racial differences in suspension rate. ## Extraversion
Extraversion
extrav <- glm(ever_suspended ~ race * sex * extra_tipi,
family = binomial, data = nlsy)
summary(extrav)##
## Call:
## glm(formula = ever_suspended ~ race * sex * extra_tipi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0169 -0.6665 -0.6237 -0.4289 2.3254
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.396656 0.249029 -5.608 2.04e-08 ***
## racelatinx 0.660512 0.606705 1.089 0.276292
## raceblack 1.060868 0.305870 3.468 0.000524 ***
## raceother 0.591624 0.483923 1.223 0.221497
## sexFEMALE -0.990242 0.428880 -2.309 0.020949 *
## extra_tipi -0.025821 0.050488 -0.511 0.609057
## racelatinx:sexFEMALE -1.384298 1.313548 -1.054 0.291947
## raceblack:sexFEMALE 0.084643 0.514272 0.165 0.869269
## raceother:sexFEMALE -0.474402 0.914336 -0.519 0.603867
## racelatinx:extra_tipi -0.053104 0.130616 -0.407 0.684325
## raceblack:extra_tipi -0.028339 0.062984 -0.450 0.652757
## raceother:extra_tipi -0.040059 0.099418 -0.403 0.686997
## sexFEMALE:extra_tipi 0.041525 0.084660 0.490 0.623786
## racelatinx:sexFEMALE:extra_tipi 0.275515 0.257534 1.070 0.284702
## raceblack:sexFEMALE:extra_tipi -0.024872 0.103430 -0.240 0.809966
## raceother:sexFEMALE:extra_tipi 0.007152 0.183292 0.039 0.968876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6924.1 on 7263 degrees of freedom
## AIC: 6956.1
##
## Number of Fisher Scoring iterations: 4plot_model(extrav, type = "pred", terms = c("extra_tipi", "race", "sex"))
object1 <- emtrends(extrav, pairwise ~ race*sex, var = "extra_tipi", adjust = "none")
object1## $emtrends
## race sex extra_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.0258 0.0505 Inf -0.125 0.0731
## latinx MALE -0.0789 0.1205 Inf -0.315 0.1572
## black MALE -0.0542 0.0377 Inf -0.128 0.0196
## other MALE -0.0659 0.0856 Inf -0.234 0.1020
## white FEMALE 0.0157 0.0680 Inf -0.117 0.1489
## latinx FEMALE 0.2381 0.2113 Inf -0.176 0.6522
## black FEMALE -0.0375 0.0460 Inf -0.128 0.0526
## other FEMALE -0.0172 0.1382 Inf -0.288 0.2536
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.05310 0.1306 Inf 0.407 0.6843
## white MALE - black MALE 0.02834 0.0630 Inf 0.450 0.6528
## white MALE - other MALE 0.04006 0.0994 Inf 0.403 0.6870
## white MALE - white FEMALE -0.04153 0.0847 Inf -0.490 0.6238
## white MALE - latinx FEMALE -0.26394 0.2172 Inf -1.215 0.2244
## white MALE - black FEMALE 0.01169 0.0683 Inf 0.171 0.8641
## white MALE - other FEMALE -0.00862 0.1471 Inf -0.059 0.9533
## latinx MALE - black MALE -0.02477 0.1262 Inf -0.196 0.8444
## latinx MALE - other MALE -0.01305 0.1478 Inf -0.088 0.9297
## latinx MALE - white FEMALE -0.09463 0.1383 Inf -0.684 0.4939
## latinx MALE - latinx FEMALE -0.31704 0.2432 Inf -1.304 0.1924
## latinx MALE - black FEMALE -0.04142 0.1289 Inf -0.321 0.7480
## latinx MALE - other FEMALE -0.06172 0.1833 Inf -0.337 0.7363
## black MALE - other MALE 0.01172 0.0936 Inf 0.125 0.9003
## black MALE - white FEMALE -0.06986 0.0777 Inf -0.899 0.3685
## black MALE - latinx FEMALE -0.29227 0.2146 Inf -1.362 0.1733
## black MALE - black FEMALE -0.01665 0.0594 Inf -0.280 0.7793
## black MALE - other FEMALE -0.03696 0.1432 Inf -0.258 0.7964
## other MALE - white FEMALE -0.08158 0.1093 Inf -0.746 0.4555
## other MALE - latinx FEMALE -0.30399 0.2280 Inf -1.333 0.1824
## other MALE - black FEMALE -0.02837 0.0972 Inf -0.292 0.7704
## other MALE - other FEMALE -0.04868 0.1626 Inf -0.299 0.7646
## white FEMALE - latinx FEMALE -0.22241 0.2220 Inf -1.002 0.3163
## white FEMALE - black FEMALE 0.05321 0.0820 Inf 0.649 0.5166
## white FEMALE - other FEMALE 0.03291 0.1540 Inf 0.214 0.8308
## latinx FEMALE - black FEMALE 0.27562 0.2162 Inf 1.275 0.2024
## latinx FEMALE - other FEMALE 0.25532 0.2525 Inf 1.011 0.3119
## black FEMALE - other FEMALE -0.02030 0.1456 Inf -0.139 0.8891extrav <- glm(ever_suspended ~ race * sex * extrav_mi,
family = binomial, data = nlsy)
summary(extrav)##
## Call:
## glm(formula = ever_suspended ~ race * sex * extrav_mi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1500 -0.7017 -0.4968 -0.4485 2.5048
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.11083 0.55925 -1.986 0.0470 *
## racelatinx 1.61512 1.78197 0.906 0.3647
## raceblack 1.21645 0.73797 1.648 0.0993 .
## raceother -0.81054 1.16200 -0.698 0.4855
## sexFEMALE -0.97618 0.89792 -1.087 0.2770
## extrav_mi -0.21630 0.15969 -1.355 0.1756
## racelatinx:sexFEMALE -4.06003 5.92839 -0.685 0.4934
## raceblack:sexFEMALE -0.57100 1.16078 -0.492 0.6228
## raceother:sexFEMALE 1.24192 1.88765 0.658 0.5106
## racelatinx:extrav_mi -0.50309 0.57322 -0.878 0.3801
## raceblack:extrav_mi 0.04593 0.21076 0.218 0.8275
## raceother:extrav_mi 0.44677 0.32004 1.396 0.1627
## sexFEMALE:extrav_mi 0.17890 0.25752 0.695 0.4872
## racelatinx:sexFEMALE:extrav_mi 0.94929 1.51057 0.628 0.5297
## raceblack:sexFEMALE:extrav_mi 0.04661 0.32994 0.141 0.8876
## raceother:sexFEMALE:extrav_mi -0.51132 0.52283 -0.978 0.3281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289.4 on 1311 degrees of freedom
## Residual deviance: 1200.4 on 1296 degrees of freedom
## (5967 observations deleted due to missingness)
## AIC: 1232.4
##
## Number of Fisher Scoring iterations: 5plot_model(extrav, type = "pred", terms = c("extrav_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="extrav_mi [all]"` to get smooth plots.
object1 <- emtrends(extrav, pairwise ~ race*sex, var = "extrav_mi", adjust = "none")
object1## $emtrends
## race sex extrav_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.2163 0.160 Inf -0.529 0.0967
## latinx MALE -0.7194 0.551 Inf -1.798 0.3596
## black MALE -0.1704 0.138 Inf -0.440 0.0992
## other MALE 0.2305 0.277 Inf -0.313 0.7741
## white FEMALE -0.0374 0.202 Inf -0.433 0.3586
## latinx FEMALE 0.4088 1.383 Inf -2.302 3.1192
## black FEMALE 0.0551 0.154 Inf -0.246 0.3564
## other FEMALE -0.1020 0.361 Inf -0.809 0.6050
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.5031 0.573 Inf 0.878 0.3801
## white MALE - black MALE -0.0459 0.211 Inf -0.218 0.8275
## white MALE - other MALE -0.4468 0.320 Inf -1.396 0.1627
## white MALE - white FEMALE -0.1789 0.258 Inf -0.695 0.4872
## white MALE - latinx FEMALE -0.6251 1.392 Inf -0.449 0.6534
## white MALE - black FEMALE -0.2714 0.222 Inf -1.225 0.2207
## white MALE - other FEMALE -0.1143 0.394 Inf -0.290 0.7719
## latinx MALE - black MALE -0.5490 0.567 Inf -0.968 0.3333
## latinx MALE - other MALE -0.9499 0.616 Inf -1.541 0.1234
## latinx MALE - white FEMALE -0.6820 0.586 Inf -1.163 0.2448
## latinx MALE - latinx FEMALE -1.1282 1.488 Inf -0.758 0.4485
## latinx MALE - black FEMALE -0.7745 0.572 Inf -1.355 0.1754
## latinx MALE - other FEMALE -0.6174 0.658 Inf -0.938 0.3482
## black MALE - other MALE -0.4008 0.310 Inf -1.295 0.1954
## black MALE - white FEMALE -0.1330 0.244 Inf -0.544 0.5864
## black MALE - latinx FEMALE -0.5792 1.390 Inf -0.417 0.6769
## black MALE - black FEMALE -0.2255 0.206 Inf -1.093 0.2742
## black MALE - other FEMALE -0.0684 0.386 Inf -0.177 0.8593
## other MALE - white FEMALE 0.2679 0.343 Inf 0.781 0.4350
## other MALE - latinx FEMALE -0.1783 1.410 Inf -0.126 0.8994
## other MALE - black FEMALE 0.1753 0.317 Inf 0.553 0.5803
## other MALE - other FEMALE 0.3324 0.455 Inf 0.731 0.4650
## white FEMALE - latinx FEMALE -0.4462 1.398 Inf -0.319 0.7495
## white FEMALE - black FEMALE -0.0925 0.254 Inf -0.365 0.7154
## white FEMALE - other FEMALE 0.0646 0.413 Inf 0.156 0.8759
## latinx FEMALE - black FEMALE 0.3537 1.391 Inf 0.254 0.7994
## latinx FEMALE - other FEMALE 0.5108 1.429 Inf 0.357 0.7208
## black FEMALE - other FEMALE 0.1571 0.392 Inf 0.401 0.6886extrav <- glm(ever_suspended ~ race * sex * extrav,
family = binomial, data = add)
summary(extrav)##
## Call:
## glm(formula = ever_suspended ~ race * sex * extrav, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3445 -0.8795 -0.5527 1.1214 2.2497
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.35879 0.25141 -5.405 6.49e-08 ***
## racelatinx 0.57331 0.67029 0.855 0.3924
## raceblack 1.95320 0.44417 4.397 1.10e-05 ***
## raceother -0.89636 1.17225 -0.765 0.4445
## sexfemale -0.94746 0.41681 -2.273 0.0230 *
## extrav 0.17387 0.07369 2.360 0.0183 *
## racelatinx:sexfemale -0.60133 1.01991 -0.590 0.5555
## raceblack:sexfemale -0.34797 0.65187 -0.534 0.5935
## raceother:sexfemale -1.06562 1.93930 -0.549 0.5827
## racelatinx:extrav -0.05969 0.19355 -0.308 0.7578
## raceblack:extrav -0.34161 0.13538 -2.523 0.0116 *
## raceother:extrav 0.20162 0.34897 0.578 0.5634
## sexfemale:extrav -0.04775 0.12021 -0.397 0.6912
## racelatinx:sexfemale:extrav 0.22655 0.29295 0.773 0.4393
## raceblack:sexfemale:extrav 0.20433 0.19425 1.052 0.2928
## raceother:sexfemale:extrav 0.40049 0.55093 0.727 0.4673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5534.3 on 5076 degrees of freedom
## AIC: 5566.3
##
## Number of Fisher Scoring iterations: 4plot_model(extrav, type = "pred", terms = c("extrav", "race", "sex"))## Data were 'prettified'. Consider using `terms="extrav [all]"` to get smooth plots.
object1 <- emtrends(extrav, pairwise ~ race*sex, var = "extrav", adjust = "none")
object1## $emtrends
## race sex extrav.trend SE df asymp.LCL asymp.UCL
## white male 0.1739 0.0737 Inf 0.0294 0.3183
## latinx male 0.1142 0.1790 Inf -0.2366 0.4650
## black male -0.1677 0.1136 Inf -0.3903 0.0549
## other male 0.3755 0.3411 Inf -0.2931 1.0440
## white female 0.1261 0.0950 Inf -0.0600 0.3123
## latinx female 0.2930 0.1983 Inf -0.0957 0.6817
## black female -0.0112 0.1019 Inf -0.2109 0.1886
## other female 0.7282 0.4156 Inf -0.0863 1.5428
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.0597 0.194 Inf 0.308 0.7578
## white male - black male 0.3416 0.135 Inf 2.523 0.0116
## white male - other male -0.2016 0.349 Inf -0.578 0.5634
## white male - white female 0.0478 0.120 Inf 0.397 0.6912
## white male - latinx female -0.1191 0.212 Inf -0.563 0.5735
## white male - black female 0.1850 0.126 Inf 1.471 0.1412
## white male - other female -0.5544 0.422 Inf -1.313 0.1890
## latinx male - black male 0.2819 0.212 Inf 1.330 0.1835
## latinx male - other male -0.2613 0.385 Inf -0.678 0.4975
## latinx male - white female -0.0119 0.203 Inf -0.059 0.9530
## latinx male - latinx female -0.1788 0.267 Inf -0.669 0.5033
## latinx male - black female 0.1253 0.206 Inf 0.609 0.5428
## latinx male - other female -0.6141 0.452 Inf -1.357 0.1748
## black male - other male -0.5432 0.360 Inf -1.511 0.1308
## black male - white female -0.2939 0.148 Inf -1.985 0.0472
## black male - latinx female -0.4607 0.229 Inf -2.016 0.0438
## black male - black female -0.1566 0.153 Inf -1.026 0.3048
## black male - other female -0.8960 0.431 Inf -2.080 0.0376
## other male - white female 0.2494 0.354 Inf 0.704 0.4813
## other male - latinx female 0.0825 0.395 Inf 0.209 0.8344
## other male - black female 0.3866 0.356 Inf 1.086 0.2774
## other male - other female -0.3527 0.538 Inf -0.656 0.5118
## white female - latinx female -0.1669 0.220 Inf -0.759 0.4480
## white female - black female 0.1373 0.139 Inf 0.985 0.3244
## white female - other female -0.6021 0.426 Inf -1.412 0.1578
## latinx female - black female 0.3041 0.223 Inf 1.364 0.1726
## latinx female - other female -0.4353 0.460 Inf -0.945 0.3446
## black female - other female -0.7394 0.428 Inf -1.728 0.0840Extraversion seems to be a risk factor for Black students in the ADD Health sample, but not in the NLSY (either through the TIPI or the Mini-IPIP)
Agreeableness
agree <- glm(ever_suspended ~ race * sex * agree_tipi,
family = binomial, data = nlsy)
summary(agree)##
## Call:
## glm(formula = ever_suspended ~ race * sex * agree_tipi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0558 -0.6758 -0.6028 -0.4169 2.2704
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.955962 0.274529 -3.482 0.000497 ***
## racelatinx -0.188975 0.839978 -0.225 0.821998
## raceblack 0.739839 0.344329 2.149 0.031663 *
## raceother 0.019125 0.544292 0.035 0.971970
## sexFEMALE -0.847834 0.502319 -1.688 0.091442 .
## agree_tipi -0.119528 0.057094 -2.094 0.036300 *
## racelatinx:sexFEMALE -0.057606 1.396756 -0.041 0.967102
## raceblack:sexFEMALE -0.102722 0.612293 -0.168 0.866767
## raceother:sexFEMALE -0.424316 1.095299 -0.387 0.698462
## racelatinx:agree_tipi 0.131325 0.159687 0.822 0.410854
## raceblack:agree_tipi 0.042739 0.071182 0.600 0.548229
## raceother:agree_tipi 0.082749 0.110751 0.747 0.454962
## sexFEMALE:agree_tipi 0.020289 0.099528 0.204 0.838469
## racelatinx:sexFEMALE:agree_tipi -0.006828 0.265868 -0.026 0.979510
## raceblack:sexFEMALE:agree_tipi 0.008036 0.120836 0.067 0.946975
## raceother:sexFEMALE:agree_tipi -0.011556 0.212876 -0.054 0.956707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6919.3 on 7263 degrees of freedom
## AIC: 6951.3
##
## Number of Fisher Scoring iterations: 5plot_model(agree, type = "pred", terms = c("agree_tipi", "race", "sex"))
object1 <- emtrends(agree, pairwise ~ race*sex, var = "agree_tipi", adjust = "none")
object1## $emtrends
## race sex agree_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.1195 0.0571 Inf -0.231 -0.00763
## latinx MALE 0.0118 0.1491 Inf -0.280 0.30409
## black MALE -0.0768 0.0425 Inf -0.160 0.00653
## other MALE -0.0368 0.0949 Inf -0.223 0.14922
## white FEMALE -0.0992 0.0815 Inf -0.259 0.06054
## latinx FEMALE 0.0253 0.1963 Inf -0.360 0.41003
## black FEMALE -0.0485 0.0537 Inf -0.154 0.05687
## other FEMALE -0.0280 0.1625 Inf -0.347 0.29044
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.13133 0.1597 Inf -0.822 0.4109
## white MALE - black MALE -0.04274 0.0712 Inf -0.600 0.5482
## white MALE - other MALE -0.08275 0.1108 Inf -0.747 0.4550
## white MALE - white FEMALE -0.02029 0.0995 Inf -0.204 0.8385
## white MALE - latinx FEMALE -0.14479 0.2044 Inf -0.708 0.4788
## white MALE - black FEMALE -0.07106 0.0784 Inf -0.906 0.3648
## white MALE - other FEMALE -0.09148 0.1722 Inf -0.531 0.5953
## latinx MALE - black MALE 0.08859 0.1551 Inf 0.571 0.5678
## latinx MALE - other MALE 0.04858 0.1768 Inf 0.275 0.7835
## latinx MALE - white FEMALE 0.11104 0.1700 Inf 0.653 0.5136
## latinx MALE - latinx FEMALE -0.01346 0.2465 Inf -0.055 0.9565
## latinx MALE - black FEMALE 0.06026 0.1585 Inf 0.380 0.7038
## latinx MALE - other FEMALE 0.03984 0.2206 Inf 0.181 0.8566
## black MALE - other MALE -0.04001 0.1040 Inf -0.385 0.7004
## black MALE - white FEMALE 0.02245 0.0919 Inf 0.244 0.8071
## black MALE - latinx FEMALE -0.10205 0.2009 Inf -0.508 0.6114
## black MALE - black FEMALE -0.02833 0.0685 Inf -0.413 0.6793
## black MALE - other FEMALE -0.04874 0.1680 Inf -0.290 0.7717
## other MALE - white FEMALE 0.06246 0.1251 Inf 0.499 0.6176
## other MALE - latinx FEMALE -0.06204 0.2181 Inf -0.285 0.7760
## other MALE - black FEMALE 0.01169 0.1091 Inf 0.107 0.9147
## other MALE - other FEMALE -0.00873 0.1882 Inf -0.046 0.9630
## white FEMALE - latinx FEMALE -0.12450 0.2126 Inf -0.586 0.5581
## white FEMALE - black FEMALE -0.05078 0.0976 Inf -0.520 0.6031
## white FEMALE - other FEMALE -0.07119 0.1818 Inf -0.392 0.6953
## latinx FEMALE - black FEMALE 0.07372 0.2035 Inf 0.362 0.7172
## latinx FEMALE - other FEMALE 0.05330 0.2548 Inf 0.209 0.8343
## black FEMALE - other FEMALE -0.02042 0.1712 Inf -0.119 0.9050agree <- glm(ever_suspended ~ race * sex * agree_mi,
family = binomial, data = nlsy)
summary(agree)##
## Call:
## glm(formula = ever_suspended ~ race * sex * agree_mi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1620 -0.7057 -0.4833 -0.3992 2.3596
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4368 0.7562 0.578 0.56349
## racelatinx 0.9524 2.9577 0.322 0.74744
## raceblack -0.4724 0.9699 -0.487 0.62621
## raceother -1.1201 1.3460 -0.832 0.40532
## sexFEMALE -1.5197 1.3684 -1.111 0.26676
## agree_mi -0.6314 0.2109 -2.994 0.00276 **
## racelatinx:sexFEMALE 10.1106 9.9604 1.015 0.31007
## raceblack:sexFEMALE 0.7608 1.6801 0.453 0.65067
## raceother:sexFEMALE 1.8385 2.4326 0.756 0.44978
## racelatinx:agree_mi -0.2591 0.8547 -0.303 0.76181
## raceblack:agree_mi 0.5099 0.2684 1.900 0.05749 .
## raceother:agree_mi 0.5129 0.3659 1.401 0.16107
## sexFEMALE:agree_mi 0.3500 0.3534 0.990 0.32200
## racelatinx:sexFEMALE:agree_mi -3.1019 3.1670 -0.979 0.32736
## raceblack:sexFEMALE:agree_mi -0.3456 0.4356 -0.794 0.42746
## raceother:sexFEMALE:agree_mi -0.6680 0.6419 -1.041 0.29801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289.4 on 1311 degrees of freedom
## Residual deviance: 1189.7 on 1296 degrees of freedom
## (5967 observations deleted due to missingness)
## AIC: 1221.7
##
## Number of Fisher Scoring iterations: 7plot_model(agree, type = "pred", terms = c("agree_mi", "race", "sex"))
object1 <- emtrends(agree, pairwise ~ race*sex, var = "agree_mi", adjust = "none")
object1## $emtrends
## race sex agree_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.631 0.211 Inf -1.045 -0.218
## latinx MALE -0.890 0.828 Inf -2.514 0.733
## black MALE -0.121 0.166 Inf -0.447 0.204
## other MALE -0.119 0.299 Inf -0.705 0.468
## white FEMALE -0.281 0.284 Inf -0.837 0.274
## latinx FEMALE -3.642 3.036 Inf -9.593 2.309
## black FEMALE -0.117 0.193 Inf -0.496 0.261
## other FEMALE -0.437 0.445 Inf -1.308 0.435
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.25907 0.855 Inf 0.303 0.7618
## white MALE - black MALE -0.50989 0.268 Inf -1.900 0.0575
## white MALE - other MALE -0.51285 0.366 Inf -1.401 0.1611
## white MALE - white FEMALE -0.34998 0.353 Inf -0.990 0.3220
## white MALE - latinx FEMALE 3.01098 3.044 Inf 0.989 0.3225
## white MALE - black FEMALE -0.51422 0.286 Inf -1.799 0.0721
## white MALE - other FEMALE -0.19484 0.492 Inf -0.396 0.6921
## latinx MALE - black MALE -0.76896 0.845 Inf -0.910 0.3627
## latinx MALE - other MALE -0.77193 0.881 Inf -0.877 0.3807
## latinx MALE - white FEMALE -0.60905 0.875 Inf -0.696 0.4866
## latinx MALE - latinx FEMALE 2.75190 3.147 Inf 0.874 0.3819
## latinx MALE - black FEMALE -0.77329 0.851 Inf -0.909 0.3632
## latinx MALE - other FEMALE -0.45392 0.940 Inf -0.483 0.6292
## black MALE - other MALE -0.00297 0.342 Inf -0.009 0.9931
## black MALE - white FEMALE 0.15991 0.329 Inf 0.487 0.6265
## black MALE - latinx FEMALE 3.52086 3.041 Inf 1.158 0.2469
## black MALE - black FEMALE -0.00433 0.255 Inf -0.017 0.9864
## black MALE - other FEMALE 0.31504 0.475 Inf 0.664 0.5068
## other MALE - white FEMALE 0.16288 0.412 Inf 0.395 0.6927
## other MALE - latinx FEMALE 3.52383 3.051 Inf 1.155 0.2481
## other MALE - black FEMALE -0.00137 0.356 Inf -0.004 0.9969
## other MALE - other FEMALE 0.31801 0.536 Inf 0.593 0.5528
## white FEMALE - latinx FEMALE 3.36095 3.049 Inf 1.102 0.2704
## white FEMALE - black FEMALE -0.16424 0.343 Inf -0.479 0.6321
## white FEMALE - other FEMALE 0.15513 0.527 Inf 0.294 0.7686
## latinx FEMALE - black FEMALE -3.52520 3.042 Inf -1.159 0.2466
## latinx FEMALE - other FEMALE -3.20582 3.069 Inf -1.045 0.2962
## black FEMALE - other FEMALE 0.31938 0.485 Inf 0.659 0.5100agree <- glm(ever_suspended ~ race * sex * agree,
family = binomial, data = add)
summary(agree)##
## Call:
## glm(formula = ever_suspended ~ race * sex * agree, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9778 -0.8544 -0.5602 1.0869 2.2536
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.13044 0.32056 0.407 0.684066
## racelatinx 1.63494 0.85378 1.915 0.055499 .
## raceblack 2.55435 0.67702 3.773 0.000161 ***
## raceother -2.40254 1.54501 -1.555 0.119938
## sexfemale -0.11163 0.61761 -0.181 0.856569
## agree -0.25339 0.08788 -2.883 0.003934 **
## racelatinx:sexfemale -1.02713 1.38277 -0.743 0.457600
## raceblack:sexfemale -1.54593 1.01509 -1.523 0.127772
## raceother:sexfemale 3.17024 2.89731 1.094 0.273867
## racelatinx:agree -0.34921 0.23508 -1.485 0.137426
## raceblack:agree -0.45181 0.18131 -2.492 0.012706 *
## raceother:agree 0.59740 0.42184 1.416 0.156726
## sexfemale:agree -0.22432 0.15960 -1.406 0.159861
## racelatinx:sexfemale:agree 0.31587 0.36558 0.864 0.387576
## raceblack:sexfemale:agree 0.47343 0.26459 1.789 0.073569 .
## raceother:sexfemale:agree -0.76842 0.75808 -1.014 0.310758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5484.0 on 5076 degrees of freedom
## AIC: 5516
##
## Number of Fisher Scoring iterations: 4plot_model(agree, type = "pred", terms = c("agree", "race", "sex"))## Data were 'prettified'. Consider using `terms="agree [all]"` to get smooth plots.
object1 <- emtrends(agree, pairwise ~ race*sex, var = "agree", adjust = "none")
object1## $emtrends
## race sex agree.trend SE df asymp.LCL asymp.UCL
## white male -0.253 0.0879 Inf -0.426 -0.0812
## latinx male -0.603 0.2180 Inf -1.030 -0.1752
## black male -0.705 0.1586 Inf -1.016 -0.3944
## other male 0.344 0.4126 Inf -0.465 1.1527
## white female -0.478 0.1332 Inf -0.739 -0.2166
## latinx female -0.511 0.2462 Inf -0.994 -0.0284
## black female -0.456 0.1392 Inf -0.729 -0.1832
## other female -0.649 0.6156 Inf -1.855 0.5579
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.3492 0.235 Inf 1.485 0.1374
## white male - black male 0.4518 0.181 Inf 2.492 0.0127
## white male - other male -0.5974 0.422 Inf -1.416 0.1567
## white male - white female 0.2243 0.160 Inf 1.406 0.1599
## white male - latinx female 0.2577 0.261 Inf 0.986 0.3244
## white male - black female 0.2027 0.165 Inf 1.231 0.2183
## white male - other female 0.3953 0.622 Inf 0.636 0.5249
## latinx male - black male 0.1026 0.270 Inf 0.381 0.7035
## latinx male - other male -0.9466 0.467 Inf -2.028 0.0425
## latinx male - white female -0.1249 0.256 Inf -0.489 0.6250
## latinx male - latinx female -0.0915 0.329 Inf -0.278 0.7807
## latinx male - black female -0.1465 0.259 Inf -0.566 0.5712
## latinx male - other female 0.0461 0.653 Inf 0.071 0.9437
## black male - other male -1.0492 0.442 Inf -2.374 0.0176
## black male - white female -0.2275 0.207 Inf -1.098 0.2721
## black male - latinx female -0.1942 0.293 Inf -0.663 0.5074
## black male - black female -0.2491 0.211 Inf -1.180 0.2378
## black male - other female -0.0565 0.636 Inf -0.089 0.9292
## other male - white female 0.8217 0.434 Inf 1.895 0.0581
## other male - latinx female 0.8551 0.480 Inf 1.780 0.0751
## other male - black female 0.8001 0.435 Inf 1.837 0.0661
## other male - other female 0.9927 0.741 Inf 1.340 0.1804
## white female - latinx female 0.0333 0.280 Inf 0.119 0.9052
## white female - black female -0.0216 0.193 Inf -0.112 0.9107
## white female - other female 0.1710 0.630 Inf 0.272 0.7860
## latinx female - black female -0.0550 0.283 Inf -0.194 0.8460
## latinx female - other female 0.1377 0.663 Inf 0.208 0.8355
## black female - other female 0.1926 0.631 Inf 0.305 0.7602Agreeableness is as predicted
Conscientiousness
consc <- glm(ever_suspended ~ race * sex * consc_tipi,
family = binomial, data = nlsy)
summary(consc)##
## Call:
## glm(formula = ever_suspended ~ race * sex * consc_tipi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9537 -0.6609 -0.6231 -0.4085 2.3149
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.41958 0.29906 -4.747 2.07e-06 ***
## racelatinx 0.87296 0.81660 1.069 0.2851
## raceblack 0.71877 0.38913 1.847 0.0647 .
## raceother -0.34204 0.62654 -0.546 0.5851
## sexFEMALE -1.35773 0.53703 -2.528 0.0115 *
## consc_tipi -0.01862 0.05425 -0.343 0.7315
## racelatinx:sexFEMALE 0.50065 1.35938 0.368 0.7127
## raceblack:sexFEMALE 0.60639 0.67153 0.903 0.3665
## raceother:sexFEMALE 1.65566 1.07956 1.534 0.1251
## racelatinx:consc_tipi -0.07686 0.14310 -0.537 0.5912
## raceblack:consc_tipi 0.03988 0.06916 0.577 0.5642
## raceother:consc_tipi 0.13581 0.11100 1.223 0.2211
## sexFEMALE:consc_tipi 0.10247 0.09471 1.082 0.2793
## racelatinx:sexFEMALE:consc_tipi -0.10053 0.23912 -0.420 0.6742
## raceblack:sexFEMALE:consc_tipi -0.11731 0.11702 -1.003 0.3161
## raceother:sexFEMALE:consc_tipi -0.38317 0.19333 -1.982 0.0475 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6924.3 on 7263 degrees of freedom
## AIC: 6956.3
##
## Number of Fisher Scoring iterations: 5plot_model(consc, type = "pred", terms = c("consc_tipi", "race", "sex"))
object1 <- emtrends(consc, pairwise ~ race*sex, var = "consc_tipi", adjust = "none")
object1## $emtrends
## race sex consc_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.01862 0.0543 Inf -0.1249 0.0877
## latinx MALE -0.09547 0.1324 Inf -0.3550 0.1641
## black MALE 0.02126 0.0429 Inf -0.0628 0.1053
## other MALE 0.11719 0.0968 Inf -0.0726 0.3070
## white FEMALE 0.08386 0.0776 Inf -0.0683 0.2360
## latinx FEMALE -0.09353 0.1751 Inf -0.4368 0.2497
## black FEMALE 0.00642 0.0537 Inf -0.0988 0.1117
## other FEMALE -0.16351 0.1379 Inf -0.4339 0.1069
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.07686 0.1431 Inf 0.537 0.5912
## white MALE - black MALE -0.03988 0.0692 Inf -0.577 0.5642
## white MALE - other MALE -0.13581 0.1110 Inf -1.223 0.2211
## white MALE - white FEMALE -0.10247 0.0947 Inf -1.082 0.2793
## white MALE - latinx FEMALE 0.07492 0.1834 Inf 0.409 0.6828
## white MALE - black FEMALE -0.02504 0.0763 Inf -0.328 0.7430
## white MALE - other FEMALE 0.14489 0.1482 Inf 0.977 0.3283
## latinx MALE - black MALE -0.11673 0.1392 Inf -0.839 0.4017
## latinx MALE - other MALE -0.21266 0.1640 Inf -1.296 0.1949
## latinx MALE - white FEMALE -0.17933 0.1535 Inf -1.168 0.2427
## latinx MALE - latinx FEMALE -0.00194 0.2196 Inf -0.009 0.9929
## latinx MALE - black FEMALE -0.10189 0.1429 Inf -0.713 0.4758
## latinx MALE - other FEMALE 0.06803 0.1912 Inf 0.356 0.7220
## black MALE - other MALE -0.09593 0.1059 Inf -0.906 0.3650
## black MALE - white FEMALE -0.06260 0.0887 Inf -0.706 0.4803
## black MALE - latinx FEMALE 0.11479 0.1803 Inf 0.637 0.5244
## black MALE - black FEMALE 0.01484 0.0687 Inf 0.216 0.8291
## black MALE - other FEMALE 0.18477 0.1445 Inf 1.279 0.2009
## other MALE - white FEMALE 0.03333 0.1241 Inf 0.269 0.7883
## other MALE - latinx FEMALE 0.21072 0.2001 Inf 1.053 0.2924
## other MALE - black FEMALE 0.11077 0.1107 Inf 1.000 0.3172
## other MALE - other FEMALE 0.28070 0.1685 Inf 1.665 0.0958
## white FEMALE - latinx FEMALE 0.17739 0.1916 Inf 0.926 0.3545
## white FEMALE - black FEMALE 0.07744 0.0944 Inf 0.820 0.4120
## white FEMALE - other FEMALE 0.24737 0.1583 Inf 1.563 0.1181
## latinx FEMALE - black FEMALE -0.09995 0.1832 Inf -0.546 0.5853
## latinx FEMALE - other FEMALE 0.06997 0.2229 Inf 0.314 0.7536
## black FEMALE - other FEMALE 0.16993 0.1480 Inf 1.148 0.2510consc <- glm(ever_suspended ~ race * sex * consc_mi,
family = binomial, data = nlsy)
summary(consc)##
## Call:
## glm(formula = ever_suspended ~ race * sex * consc_mi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3348 -0.7071 -0.5096 -0.4297 2.4134
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.679568 0.720410 -0.943 0.3455
## racelatinx -0.321115 2.330951 -0.138 0.8904
## raceblack 1.764276 0.983091 1.795 0.0727 .
## raceother -0.006714 1.577306 -0.004 0.9966
## sexFEMALE -1.025111 1.168149 -0.878 0.3802
## consc_mi -0.321710 0.196491 -1.637 0.1016
## racelatinx:sexFEMALE 1.019142 5.039534 0.202 0.8397
## raceblack:sexFEMALE -1.519556 1.559976 -0.974 0.3300
## raceother:sexFEMALE -2.081622 2.783614 -0.748 0.4546
## racelatinx:consc_mi 0.126679 0.601948 0.210 0.8333
## raceblack:consc_mi -0.090817 0.262998 -0.345 0.7299
## raceother:consc_mi 0.210786 0.408452 0.516 0.6058
## sexFEMALE:consc_mi 0.188806 0.308149 0.613 0.5401
## racelatinx:sexFEMALE:consc_mi -0.456209 1.235849 -0.369 0.7120
## raceblack:sexFEMALE:consc_mi 0.277417 0.405715 0.684 0.4941
## raceother:sexFEMALE:consc_mi 0.384659 0.705933 0.545 0.5858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289.4 on 1311 degrees of freedom
## Residual deviance: 1196.6 on 1296 degrees of freedom
## (5967 observations deleted due to missingness)
## AIC: 1228.6
##
## Number of Fisher Scoring iterations: 5plot_model(consc, type = "pred", terms = c("consc_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="consc_mi [all]"` to get smooth plots.
object1 <- emtrends(consc, pairwise ~ race*sex, var = "consc_mi", adjust = "none")
object1## $emtrends
## race sex consc_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.3217 0.196 Inf -0.707 0.0634
## latinx MALE -0.1950 0.569 Inf -1.310 0.9201
## black MALE -0.4125 0.175 Inf -0.755 -0.0699
## other MALE -0.1109 0.358 Inf -0.813 0.5909
## white FEMALE -0.1329 0.237 Inf -0.598 0.3323
## latinx FEMALE -0.4624 1.053 Inf -2.526 1.6012
## black FEMALE 0.0537 0.198 Inf -0.334 0.4412
## other FEMALE 0.4625 0.525 Inf -0.566 1.4907
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.1267 0.602 Inf -0.210 0.8333
## white MALE - black MALE 0.0908 0.263 Inf 0.345 0.7299
## white MALE - other MALE -0.2108 0.408 Inf -0.516 0.6058
## white MALE - white FEMALE -0.1888 0.308 Inf -0.613 0.5401
## white MALE - latinx FEMALE 0.1407 1.071 Inf 0.131 0.8955
## white MALE - black FEMALE -0.3754 0.279 Inf -1.347 0.1781
## white MALE - other FEMALE -0.7843 0.560 Inf -1.400 0.1615
## latinx MALE - black MALE 0.2175 0.595 Inf 0.365 0.7148
## latinx MALE - other MALE -0.0841 0.672 Inf -0.125 0.9004
## latinx MALE - white FEMALE -0.0621 0.617 Inf -0.101 0.9197
## latinx MALE - latinx FEMALE 0.2674 1.197 Inf 0.223 0.8232
## latinx MALE - black FEMALE -0.2487 0.602 Inf -0.413 0.6797
## latinx MALE - other FEMALE -0.6576 0.774 Inf -0.850 0.3955
## black MALE - other MALE -0.3016 0.398 Inf -0.757 0.4491
## black MALE - white FEMALE -0.2796 0.295 Inf -0.949 0.3429
## black MALE - latinx FEMALE 0.0499 1.067 Inf 0.047 0.9627
## black MALE - black FEMALE -0.4662 0.264 Inf -1.767 0.0773
## black MALE - other FEMALE -0.8751 0.553 Inf -1.583 0.1135
## other MALE - white FEMALE 0.0220 0.430 Inf 0.051 0.9592
## other MALE - latinx FEMALE 0.3515 1.112 Inf 0.316 0.7520
## other MALE - black FEMALE -0.1646 0.409 Inf -0.402 0.6874
## other MALE - other FEMALE -0.5735 0.635 Inf -0.903 0.3666
## white FEMALE - latinx FEMALE 0.3295 1.079 Inf 0.305 0.7601
## white FEMALE - black FEMALE -0.1866 0.309 Inf -0.604 0.5458
## white FEMALE - other FEMALE -0.5954 0.576 Inf -1.034 0.3011
## latinx FEMALE - black FEMALE -0.5161 1.071 Inf -0.482 0.6300
## latinx FEMALE - other FEMALE -0.9250 1.176 Inf -0.786 0.4317
## black FEMALE - other FEMALE -0.4088 0.561 Inf -0.729 0.4658consc <- glm(ever_suspended ~ race * sex * consc,
family = binomial, data = add)
summary(consc)##
## Call:
## glm(formula = ever_suspended ~ race * sex * consc, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4300 -0.8688 -0.5558 1.1230 2.2265
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17877 0.29273 -0.611 0.5414
## racelatinx 1.83699 0.89949 2.042 0.0411 *
## raceblack 1.35162 0.62855 2.150 0.0315 *
## raceother -0.04971 1.23090 -0.040 0.9678
## sexfemale -0.91354 0.47121 -1.939 0.0525 .
## consc -0.17172 0.08186 -2.098 0.0359 *
## racelatinx:sexfemale -1.34553 1.28329 -1.049 0.2944
## raceblack:sexfemale -1.06816 0.87588 -1.220 0.2226
## raceother:sexfemale 1.31582 2.12203 0.620 0.5352
## racelatinx:consc -0.39809 0.24768 -1.607 0.1080
## raceblack:consc -0.12641 0.16941 -0.746 0.4556
## raceother:consc -0.05899 0.34802 -0.169 0.8654
## sexfemale:consc -0.04287 0.12916 -0.332 0.7400
## racelatinx:sexfemale:consc 0.41158 0.34832 1.182 0.2374
## raceblack:sexfemale:consc 0.35997 0.23415 1.537 0.1242
## raceother:sexfemale:consc -0.23941 0.57904 -0.413 0.6793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5529.3 on 5076 degrees of freedom
## AIC: 5561.3
##
## Number of Fisher Scoring iterations: 4plot_model(consc, type = "pred", terms = c("consc", "race", "sex"))
object1 <- emtrends(consc, pairwise ~ race*sex, var = "consc", adjust = "none")
object1## $emtrends
## race sex consc.trend SE df asymp.LCL asymp.UCL
## white male -0.172 0.0819 Inf -0.332 -0.01128
## latinx male -0.570 0.2338 Inf -1.028 -0.11165
## black male -0.298 0.1483 Inf -0.589 -0.00743
## other male -0.231 0.3383 Inf -0.894 0.43226
## white female -0.215 0.0999 Inf -0.410 -0.01878
## latinx female -0.201 0.2236 Inf -0.639 0.23716
## black female 0.019 0.1271 Inf -0.230 0.26801
## other female -0.513 0.4519 Inf -1.399 0.37268
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.3981 0.248 Inf 1.607 0.1080
## white male - black male 0.1264 0.169 Inf 0.746 0.4556
## white male - other male 0.0590 0.348 Inf 0.169 0.8654
## white male - white female 0.0429 0.129 Inf 0.332 0.7400
## white male - latinx female 0.0294 0.238 Inf 0.123 0.9018
## white male - black female -0.1907 0.151 Inf -1.262 0.2071
## white male - other female 0.3413 0.459 Inf 0.743 0.4574
## latinx male - black male -0.2717 0.277 Inf -0.981 0.3264
## latinx male - other male -0.3391 0.411 Inf -0.825 0.4095
## latinx male - white female -0.3552 0.254 Inf -1.397 0.1623
## latinx male - latinx female -0.3687 0.323 Inf -1.140 0.2544
## latinx male - black female -0.5888 0.266 Inf -2.213 0.0269
## latinx male - other female -0.0568 0.509 Inf -0.112 0.9111
## black male - other male -0.0674 0.369 Inf -0.183 0.8552
## black male - white female -0.0835 0.179 Inf -0.467 0.6404
## black male - latinx female -0.0970 0.268 Inf -0.362 0.7176
## black male - black female -0.3171 0.195 Inf -1.624 0.1044
## black male - other female 0.2149 0.476 Inf 0.452 0.6514
## other male - white female -0.0161 0.353 Inf -0.046 0.9635
## other male - latinx female -0.0296 0.405 Inf -0.073 0.9418
## other male - black female -0.2497 0.361 Inf -0.691 0.4896
## other male - other female 0.2823 0.564 Inf 0.500 0.6170
## white female - latinx female -0.0135 0.245 Inf -0.055 0.9561
## white female - black female -0.2336 0.162 Inf -1.445 0.1485
## white female - other female 0.2984 0.463 Inf 0.645 0.5191
## latinx female - black female -0.2201 0.257 Inf -0.856 0.3922
## latinx female - other female 0.3119 0.504 Inf 0.619 0.5362
## black female - other female 0.5320 0.469 Inf 1.133 0.2571Emotional Stability (Neuro)
neuro <- glm(ever_suspended ~ race * sex * neuro_tipi,
family = binomial, data = nlsy)
summary(neuro)##
## Call:
## glm(formula = ever_suspended ~ race * sex * neuro_tipi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1141 -0.6994 -0.5675 -0.4116 2.2989
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.36097 0.26235 -1.376 0.168851
## racelatinx -0.40947 0.75910 -0.539 0.589601
## raceblack 0.31746 0.32894 0.965 0.334493
## raceother -0.16408 0.52721 -0.311 0.755627
## sexFEMALE -1.52112 0.41690 -3.649 0.000264 ***
## neuro_tipi -0.23063 0.05166 -4.465 8.02e-06 ***
## racelatinx:sexFEMALE 0.53939 1.22461 0.440 0.659602
## raceblack:sexFEMALE 0.61545 0.51519 1.195 0.232244
## raceother:sexFEMALE -0.57823 0.94319 -0.613 0.539839
## racelatinx:neuro_tipi 0.17020 0.14358 1.185 0.235855
## raceblack:neuro_tipi 0.12348 0.06434 1.919 0.054966 .
## raceother:neuro_tipi 0.11432 0.10230 1.117 0.263800
## sexFEMALE:neuro_tipi 0.14002 0.08462 1.655 0.097989 .
## racelatinx:sexFEMALE:neuro_tipi -0.11485 0.24093 -0.477 0.633594
## raceblack:sexFEMALE:neuro_tipi -0.13180 0.10418 -1.265 0.205813
## raceother:sexFEMALE:neuro_tipi 0.03205 0.18575 0.173 0.862996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6893.6 on 7263 degrees of freedom
## AIC: 6925.6
##
## Number of Fisher Scoring iterations: 5plot_model(neuro, type = "pred", terms = c("neuro_tipi", "race", "sex"))
object1 <- emtrends(neuro, pairwise ~ race*sex, var = "neuro_tipi", adjust = "none")
object1## $emtrends
## race sex neuro_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.2306 0.0517 Inf -0.332 -0.12938
## latinx MALE -0.0604 0.1340 Inf -0.323 0.20213
## black MALE -0.1072 0.0384 Inf -0.182 -0.03197
## other MALE -0.1163 0.0883 Inf -0.289 0.05675
## white FEMALE -0.0906 0.0670 Inf -0.222 0.04076
## latinx FEMALE -0.0353 0.1815 Inf -0.391 0.32048
## black FEMALE -0.0989 0.0471 Inf -0.191 -0.00657
## other FEMALE 0.0558 0.1398 Inf -0.218 0.32978
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.17020 0.1436 Inf -1.185 0.2359
## white MALE - black MALE -0.12348 0.0643 Inf -1.919 0.0550
## white MALE - other MALE -0.11432 0.1023 Inf -1.117 0.2638
## white MALE - white FEMALE -0.14002 0.0846 Inf -1.655 0.0980
## white MALE - latinx FEMALE -0.19537 0.1887 Inf -1.035 0.3005
## white MALE - black FEMALE -0.13170 0.0699 Inf -1.884 0.0596
## white MALE - other FEMALE -0.28639 0.1490 Inf -1.922 0.0547
## latinx MALE - black MALE 0.04672 0.1393 Inf 0.335 0.7374
## latinx MALE - other MALE 0.05588 0.1604 Inf 0.348 0.7276
## latinx MALE - white FEMALE 0.03017 0.1498 Inf 0.201 0.8404
## latinx MALE - latinx FEMALE -0.02518 0.2256 Inf -0.112 0.9111
## latinx MALE - black FEMALE 0.03850 0.1420 Inf 0.271 0.7863
## latinx MALE - other FEMALE -0.11620 0.1936 Inf -0.600 0.5484
## black MALE - other MALE 0.00916 0.0963 Inf 0.095 0.9242
## black MALE - white FEMALE -0.01654 0.0772 Inf -0.214 0.8304
## black MALE - latinx FEMALE -0.07189 0.1855 Inf -0.388 0.6983
## black MALE - black FEMALE -0.00822 0.0608 Inf -0.135 0.8924
## black MALE - other FEMALE -0.16292 0.1450 Inf -1.124 0.2611
## other MALE - white FEMALE -0.02571 0.1109 Inf -0.232 0.8166
## other MALE - latinx FEMALE -0.08106 0.2018 Inf -0.402 0.6880
## other MALE - black FEMALE -0.01738 0.1001 Inf -0.174 0.8621
## other MALE - other FEMALE -0.17208 0.1654 Inf -1.041 0.2980
## white FEMALE - latinx FEMALE -0.05535 0.1935 Inf -0.286 0.7748
## white FEMALE - black FEMALE 0.00832 0.0819 Inf 0.102 0.9191
## white FEMALE - other FEMALE -0.14637 0.1550 Inf -0.944 0.3451
## latinx FEMALE - black FEMALE 0.06367 0.1875 Inf 0.340 0.7342
## latinx FEMALE - other FEMALE -0.09102 0.2291 Inf -0.397 0.6912
## black FEMALE - other FEMALE -0.15469 0.1475 Inf -1.049 0.2944neuro <- glm(ever_suspended ~ race * sex * neuro_mi,
family = binomial, data = nlsy)
summary(neuro)##
## Call:
## glm(formula = ever_suspended ~ race * sex * neuro_mi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3759 -0.6647 -0.5224 -0.3882 2.6521
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7218 0.7768 -0.929 0.3528
## racelatinx 2.5086 2.2083 1.136 0.2560
## raceblack 2.3426 1.0137 2.311 0.0208 *
## raceother 0.9839 1.5467 0.636 0.5247
## sexFEMALE -0.1298 1.0991 -0.118 0.9060
## neuro_mi -0.3002 0.2052 -1.463 0.1435
## racelatinx:sexFEMALE -3.1511 4.8214 -0.654 0.5134
## raceblack:sexFEMALE -1.7225 1.4043 -1.227 0.2200
## raceother:sexFEMALE 0.1344 2.5354 0.053 0.9577
## racelatinx:neuro_mi -0.7544 0.6681 -1.129 0.2588
## raceblack:neuro_mi -0.2825 0.2725 -1.036 0.3000
## raceother:neuro_mi -0.0829 0.4225 -0.196 0.8444
## sexFEMALE:neuro_mi -0.1114 0.3123 -0.357 0.7213
## racelatinx:sexFEMALE:neuro_mi 0.7399 1.4384 0.514 0.6070
## raceblack:sexFEMALE:neuro_mi 0.3830 0.4004 0.956 0.3389
## raceother:sexFEMALE:neuro_mi -0.2080 0.7538 -0.276 0.7826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289.4 on 1311 degrees of freedom
## Residual deviance: 1181.2 on 1296 degrees of freedom
## (5967 observations deleted due to missingness)
## AIC: 1213.2
##
## Number of Fisher Scoring iterations: 5plot_model(neuro, type = "pred", terms = c("neuro_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="neuro_mi [all]"` to get smooth plots.
object1 <- emtrends(neuro, pairwise ~ race*sex, var = "neuro_mi", adjust = "none")
object1## $emtrends
## race sex neuro_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.300 0.205 Inf -0.702 0.1020
## latinx MALE -1.055 0.636 Inf -2.301 0.1914
## black MALE -0.583 0.179 Inf -0.934 -0.2312
## other MALE -0.383 0.369 Inf -1.107 0.3407
## white FEMALE -0.412 0.235 Inf -0.873 0.0497
## latinx FEMALE -0.426 1.252 Inf -2.880 2.0276
## black FEMALE -0.311 0.175 Inf -0.654 0.0322
## other FEMALE -0.703 0.578 Inf -1.836 0.4307
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.7544 0.668 Inf 1.129 0.2588
## white MALE - black MALE 0.2825 0.273 Inf 1.036 0.3000
## white MALE - other MALE 0.0829 0.423 Inf 0.196 0.8444
## white MALE - white FEMALE 0.1114 0.312 Inf 0.357 0.7213
## white MALE - latinx FEMALE 0.1259 1.269 Inf 0.099 0.9209
## white MALE - black FEMALE 0.0109 0.270 Inf 0.040 0.9677
## white MALE - other FEMALE 0.4023 0.614 Inf 0.656 0.5120
## latinx MALE - black MALE -0.4720 0.661 Inf -0.714 0.4749
## latinx MALE - other MALE -0.6715 0.735 Inf -0.913 0.3611
## latinx MALE - white FEMALE -0.6431 0.678 Inf -0.949 0.3429
## latinx MALE - latinx FEMALE -0.6285 1.404 Inf -0.448 0.6544
## latinx MALE - black FEMALE -0.7435 0.659 Inf -1.127 0.2595
## latinx MALE - other FEMALE -0.3521 0.859 Inf -0.410 0.6820
## black MALE - other MALE -0.1996 0.411 Inf -0.486 0.6269
## black MALE - white FEMALE -0.1711 0.296 Inf -0.578 0.5632
## black MALE - latinx FEMALE -0.1566 1.265 Inf -0.124 0.9015
## black MALE - black FEMALE -0.2715 0.251 Inf -1.083 0.2787
## black MALE - other FEMALE 0.1198 0.605 Inf 0.198 0.8431
## other MALE - white FEMALE 0.0285 0.438 Inf 0.065 0.9481
## other MALE - latinx FEMALE 0.0430 1.305 Inf 0.033 0.9737
## other MALE - black FEMALE -0.0720 0.409 Inf -0.176 0.8602
## other MALE - other FEMALE 0.3194 0.686 Inf 0.466 0.6415
## white FEMALE - latinx FEMALE 0.0145 1.274 Inf 0.011 0.9909
## white FEMALE - black FEMALE -0.1005 0.293 Inf -0.342 0.7320
## white FEMALE - other FEMALE 0.2909 0.624 Inf 0.466 0.6412
## latinx FEMALE - black FEMALE -0.1150 1.264 Inf -0.091 0.9275
## latinx FEMALE - other FEMALE 0.2764 1.379 Inf 0.200 0.8411
## black FEMALE - other FEMALE 0.3914 0.604 Inf 0.648 0.5171neuro <- glm(ever_suspended ~ race * sex * neuro,
family = binomial, data = add)
summary(neuro)##
## Call:
## glm(formula = ever_suspended ~ race * sex * neuro, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8196 -0.8245 -0.5568 0.9898 2.2853
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.62753 0.30741 2.041 0.041214 *
## racelatinx -0.19671 0.80495 -0.244 0.806944
## raceblack 2.19470 0.61965 3.542 0.000397 ***
## raceother 1.77280 1.34454 1.319 0.187333
## sexfemale -1.12807 0.44363 -2.543 0.010997 *
## neuro -0.39775 0.08585 -4.633 3.6e-06 ***
## racelatinx:sexfemale 0.90559 1.08867 0.832 0.405502
## raceblack:sexfemale -0.74604 0.80287 -0.929 0.352774
## raceother:sexfemale -0.45406 1.92389 -0.236 0.813423
## racelatinx:neuro 0.15868 0.22835 0.695 0.487113
## raceblack:neuro -0.39009 0.17393 -2.243 0.024908 *
## raceother:neuro -0.58033 0.38565 -1.505 0.132374
## sexfemale:neuro -0.03058 0.13123 -0.233 0.815722
## racelatinx:sexfemale:neuro -0.23107 0.32681 -0.707 0.479530
## raceblack:sexfemale:neuro 0.28915 0.23534 1.229 0.219217
## raceother:sexfemale:neuro 0.21578 0.57847 0.373 0.709132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5444.1 on 5076 degrees of freedom
## AIC: 5476.1
##
## Number of Fisher Scoring iterations: 4plot_model(neuro, type = "pred", terms = c("neuro", "race", "sex"))## Data were 'prettified'. Consider using `terms="neuro [all]"` to get smooth plots.
object1 <- emtrends(neuro, pairwise ~ race*sex, var = "neuro", adjust = "none")
object1## $emtrends
## race sex neuro.trend SE df asymp.LCL asymp.UCL
## white male -0.398 0.0859 Inf -0.566 -0.2295
## latinx male -0.239 0.2116 Inf -0.654 0.1757
## black male -0.788 0.1513 Inf -1.084 -0.4914
## other male -0.978 0.3760 Inf -1.715 -0.2412
## white female -0.428 0.0992 Inf -0.623 -0.2338
## latinx female -0.501 0.2117 Inf -0.916 -0.0858
## black female -0.529 0.1236 Inf -0.772 -0.2869
## other female -0.793 0.4196 Inf -1.615 0.0295
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.15868 0.228 Inf -0.695 0.4871
## white male - black male 0.39009 0.174 Inf 2.243 0.0249
## white male - other male 0.58033 0.386 Inf 1.505 0.1324
## white male - white female 0.03058 0.131 Inf 0.233 0.8157
## white male - latinx female 0.10298 0.228 Inf 0.451 0.6521
## white male - black female 0.13152 0.151 Inf 0.874 0.3822
## white male - other female 0.39513 0.428 Inf 0.923 0.3562
## latinx male - black male 0.54877 0.260 Inf 2.110 0.0349
## latinx male - other male 0.73901 0.431 Inf 1.713 0.0867
## latinx male - white female 0.18927 0.234 Inf 0.810 0.4181
## latinx male - latinx female 0.26166 0.299 Inf 0.874 0.3820
## latinx male - black female 0.29021 0.245 Inf 1.184 0.2363
## latinx male - other female 0.55381 0.470 Inf 1.179 0.2386
## black male - other male 0.19024 0.405 Inf 0.469 0.6388
## black male - white female -0.35951 0.181 Inf -1.987 0.0469
## black male - latinx female -0.28711 0.260 Inf -1.104 0.2698
## black male - black female -0.25856 0.195 Inf -1.324 0.1857
## black male - other female 0.00504 0.446 Inf 0.011 0.9910
## other male - white female -0.54975 0.389 Inf -1.414 0.1574
## other male - latinx female -0.47735 0.431 Inf -1.106 0.2686
## other male - black female -0.44881 0.396 Inf -1.134 0.2568
## other male - other female -0.18520 0.563 Inf -0.329 0.7424
## white female - latinx female 0.07239 0.234 Inf 0.310 0.7568
## white female - black female 0.10094 0.159 Inf 0.637 0.5243
## white female - other female 0.36455 0.431 Inf 0.846 0.3978
## latinx female - black female 0.02855 0.245 Inf 0.116 0.9073
## latinx female - other female 0.29216 0.470 Inf 0.622 0.5342
## black female - other female 0.26361 0.437 Inf 0.603 0.5467Openness
open <- glm(ever_suspended ~ race * sex * open_tipi,
family = binomial, data = nlsy)
summary(open)##
## Call:
## glm(formula = ever_suspended ~ race * sex * open_tipi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9751 -0.6641 -0.6134 -0.4027 2.2908
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.82017 0.33871 -5.374 7.71e-08 ***
## racelatinx 0.28326 0.89954 0.315 0.75284
## raceblack 1.34235 0.41345 3.247 0.00117 **
## raceother 0.14360 0.64208 0.224 0.82304
## sexFEMALE 0.35193 0.56709 0.621 0.53487
## open_tipi 0.05448 0.06002 0.908 0.36410
## racelatinx:sexFEMALE -0.99953 1.50237 -0.665 0.50586
## raceblack:sexFEMALE -1.07438 0.67901 -1.582 0.11359
## raceother:sexFEMALE -1.30834 1.20210 -1.088 0.27643
## racelatinx:open_tipi 0.02729 0.15815 0.173 0.86300
## raceblack:open_tipi -0.07323 0.07344 -0.997 0.31867
## raceother:open_tipi 0.04664 0.11267 0.414 0.67891
## sexFEMALE:open_tipi -0.20883 0.10239 -2.040 0.04140 *
## racelatinx:sexFEMALE:open_tipi 0.17668 0.27099 0.652 0.51442
## raceblack:sexFEMALE:open_tipi 0.18776 0.12253 1.532 0.12541
## raceother:sexFEMALE:open_tipi 0.15822 0.21217 0.746 0.45583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6922.8 on 7263 degrees of freedom
## AIC: 6954.8
##
## Number of Fisher Scoring iterations: 5plot_model(open, type = "pred", terms = c("open_tipi", "race", "sex"))
object1 <- emtrends(open, pairwise ~ race*sex, var = "open_tipi", adjust = "none")
object1## $emtrends
## race sex open_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE 0.0545 0.0600 Inf -0.0632 0.17212
## latinx MALE 0.0818 0.1463 Inf -0.2050 0.36855
## black MALE -0.0188 0.0423 Inf -0.1017 0.06418
## other MALE 0.1011 0.0953 Inf -0.0858 0.28800
## white FEMALE -0.1544 0.0830 Inf -0.3169 0.00823
## latinx FEMALE 0.0496 0.2038 Inf -0.3499 0.44908
## black FEMALE -0.0398 0.0523 Inf -0.1424 0.06273
## other FEMALE 0.0505 0.1595 Inf -0.2621 0.36312
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.027290 0.1582 Inf -0.173 0.8630
## white MALE - black MALE 0.073232 0.0734 Inf 0.997 0.3187
## white MALE - other MALE -0.046639 0.1127 Inf -0.414 0.6789
## white MALE - white FEMALE 0.208830 0.1024 Inf 2.040 0.0414
## white MALE - latinx FEMALE 0.004865 0.2125 Inf 0.023 0.9817
## white MALE - black FEMALE 0.094299 0.0796 Inf 1.184 0.2363
## white MALE - other FEMALE 0.003970 0.1704 Inf 0.023 0.9814
## latinx MALE - black MALE 0.100522 0.1523 Inf 0.660 0.5093
## latinx MALE - other MALE -0.019349 0.1746 Inf -0.111 0.9118
## latinx MALE - white FEMALE 0.236120 0.1682 Inf 1.404 0.1604
## latinx MALE - latinx FEMALE 0.032155 0.2509 Inf 0.128 0.8980
## latinx MALE - black FEMALE 0.121589 0.1554 Inf 0.782 0.4340
## latinx MALE - other FEMALE 0.031260 0.2164 Inf 0.144 0.8852
## black MALE - other MALE -0.119872 0.1043 Inf -1.149 0.2505
## black MALE - white FEMALE 0.135598 0.0931 Inf 1.456 0.1454
## black MALE - latinx FEMALE -0.068368 0.2082 Inf -0.328 0.7426
## black MALE - black FEMALE 0.021067 0.0673 Inf 0.313 0.7542
## black MALE - other FEMALE -0.069262 0.1650 Inf -0.420 0.6747
## other MALE - white FEMALE 0.255470 0.1264 Inf 2.021 0.0432
## other MALE - latinx FEMALE 0.051504 0.2250 Inf 0.229 0.8190
## other MALE - black FEMALE 0.140938 0.1088 Inf 1.296 0.1950
## other MALE - other FEMALE 0.050609 0.1858 Inf 0.272 0.7854
## white FEMALE - latinx FEMALE -0.203966 0.2200 Inf -0.927 0.3540
## white FEMALE - black FEMALE -0.114531 0.0981 Inf -1.168 0.2429
## white FEMALE - other FEMALE -0.204860 0.1798 Inf -1.140 0.2545
## latinx FEMALE - black FEMALE 0.089434 0.2104 Inf 0.425 0.6708
## latinx FEMALE - other FEMALE -0.000895 0.2588 Inf -0.003 0.9972
## black FEMALE - other FEMALE -0.090329 0.1679 Inf -0.538 0.5905open <- glm(ever_suspended ~ race * sex * open_mi,
family = binomial, data = nlsy)
summary(open)##
## Call:
## glm(formula = ever_suspended ~ race * sex * open_mi, family = binomial,
## data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1565 -0.7088 -0.5026 -0.3929 2.5772
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06397 0.70619 0.091 0.92782
## racelatinx 2.59401 2.90491 0.893 0.37187
## raceblack 0.09634 0.92991 0.104 0.91749
## raceother -1.24065 1.28911 -0.962 0.33584
## sexFEMALE -1.05770 1.20206 -0.880 0.37891
## open_mi -0.51731 0.19173 -2.698 0.00697 **
## racelatinx:sexFEMALE -0.72753 5.50295 -0.132 0.89482
## raceblack:sexFEMALE 0.21465 1.52389 0.141 0.88799
## raceother:sexFEMALE -0.68844 2.50619 -0.275 0.78355
## racelatinx:open_mi -0.67114 0.80160 -0.837 0.40245
## raceblack:open_mi 0.34963 0.24808 1.409 0.15874
## raceother:open_mi 0.53317 0.33085 1.612 0.10707
## sexFEMALE:open_mi 0.18895 0.32443 0.582 0.56030
## racelatinx:sexFEMALE:open_mi -0.14399 1.69117 -0.085 0.93215
## raceblack:sexFEMALE:open_mi -0.17344 0.40780 -0.425 0.67062
## raceother:sexFEMALE:open_mi 0.03789 0.65037 0.058 0.95355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289 on 1310 degrees of freedom
## Residual deviance: 1192 on 1295 degrees of freedom
## (5968 observations deleted due to missingness)
## AIC: 1224
##
## Number of Fisher Scoring iterations: 6plot_model(open, type = "pred", terms = c("open_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="open_mi [all]"` to get smooth plots.
object1 <- emtrends(open, pairwise ~ race*sex, var = "open_mi", adjust = "none")
object1## $emtrends
## race sex open_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.5173 0.192 Inf -0.893 -0.142
## latinx MALE -1.1884 0.778 Inf -2.714 0.337
## black MALE -0.1677 0.157 Inf -0.476 0.141
## other MALE 0.0159 0.270 Inf -0.513 0.544
## white FEMALE -0.3284 0.262 Inf -0.841 0.185
## latinx FEMALE -1.1435 1.466 Inf -4.017 1.730
## black FEMALE -0.1522 0.190 Inf -0.525 0.221
## other FEMALE 0.2427 0.495 Inf -0.727 1.213
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.6711 0.802 Inf 0.837 0.4025
## white MALE - black MALE -0.3496 0.248 Inf -1.409 0.1587
## white MALE - other MALE -0.5332 0.331 Inf -1.612 0.1071
## white MALE - white FEMALE -0.1889 0.324 Inf -0.582 0.5603
## white MALE - latinx FEMALE 0.6262 1.478 Inf 0.424 0.6719
## white MALE - black FEMALE -0.3651 0.270 Inf -1.351 0.1766
## white MALE - other FEMALE -0.7600 0.531 Inf -1.432 0.1522
## latinx MALE - black MALE -1.0208 0.794 Inf -1.285 0.1986
## latinx MALE - other MALE -1.2043 0.824 Inf -1.462 0.1437
## latinx MALE - white FEMALE -0.8601 0.821 Inf -1.047 0.2949
## latinx MALE - latinx FEMALE -0.0450 1.660 Inf -0.027 0.9784
## latinx MALE - black FEMALE -1.0363 0.801 Inf -1.293 0.1959
## latinx MALE - other FEMALE -1.4311 0.922 Inf -1.552 0.1208
## black MALE - other MALE -0.1835 0.312 Inf -0.588 0.5566
## black MALE - white FEMALE 0.1607 0.305 Inf 0.526 0.5988
## black MALE - latinx FEMALE 0.9758 1.474 Inf 0.662 0.5081
## black MALE - black FEMALE -0.0155 0.247 Inf -0.063 0.9499
## black MALE - other FEMALE -0.4104 0.519 Inf -0.790 0.4295
## other MALE - white FEMALE 0.3442 0.376 Inf 0.916 0.3596
## other MALE - latinx FEMALE 1.1593 1.491 Inf 0.778 0.4367
## other MALE - black FEMALE 0.1680 0.330 Inf 0.509 0.6107
## other MALE - other FEMALE -0.2268 0.564 Inf -0.402 0.6874
## white FEMALE - latinx FEMALE 0.8151 1.489 Inf 0.547 0.5841
## white FEMALE - black FEMALE -0.1762 0.324 Inf -0.544 0.5862
## white FEMALE - other FEMALE -0.5711 0.560 Inf -1.020 0.3078
## latinx FEMALE - black FEMALE -0.9913 1.478 Inf -0.671 0.5025
## latinx FEMALE - other FEMALE -1.3862 1.547 Inf -0.896 0.3703
## black FEMALE - other FEMALE -0.3949 0.530 Inf -0.745 0.4566open <- glm(ever_suspended ~ race * sex * open,
family = binomial, data = add)
summary(open)##
## Call:
## glm(formula = ever_suspended ~ race * sex * open, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7021 -0.8446 -0.5555 1.1015 2.2688
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02938 0.32393 0.091 0.927731
## racelatinx 0.37622 0.87801 0.428 0.668297
## raceblack 2.15089 0.63512 3.387 0.000708 ***
## raceother 0.15140 1.56471 0.097 0.922918
## sexfemale -0.79047 0.52769 -1.498 0.134141
## open -0.21916 0.08635 -2.538 0.011146 *
## racelatinx:sexfemale 0.51999 1.31279 0.396 0.692034
## raceblack:sexfemale -0.42559 0.91004 -0.468 0.640024
## raceother:sexfemale -3.68237 2.38896 -1.541 0.123216
## racelatinx:open -0.00513 0.24112 -0.021 0.983024
## raceblack:open -0.35199 0.16940 -2.078 0.037723 *
## raceother:open -0.10799 0.41894 -0.258 0.796590
## sexfemale:open -0.09762 0.14600 -0.669 0.503711
## racelatinx:sexfemale:open -0.10173 0.36780 -0.277 0.782101
## raceblack:sexfemale:open 0.19031 0.24936 0.763 0.445355
## raceother:sexfemale:open 1.14386 0.64760 1.766 0.077345 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5502.6 on 5076 degrees of freedom
## AIC: 5534.6
##
## Number of Fisher Scoring iterations: 4plot_model(open, type = "pred", terms = c("open", "race", "sex"))## Data were 'prettified'. Consider using `terms="open [all]"` to get smooth plots.
object1 <- emtrends(open, pairwise ~ race*sex, var = "open", adjust = "none")
object1## $emtrends
## race sex open.trend SE df asymp.LCL asymp.UCL
## white male -0.219 0.0863 Inf -0.388 -0.0499
## latinx male -0.224 0.2251 Inf -0.666 0.2169
## black male -0.571 0.1457 Inf -0.857 -0.2855
## other male -0.327 0.4099 Inf -1.131 0.4763
## white female -0.317 0.1177 Inf -0.548 -0.0861
## latinx female -0.424 0.2516 Inf -0.917 0.0694
## black female -0.478 0.1401 Inf -0.753 -0.2039
## other female 0.719 0.4796 Inf -0.221 1.6591
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.00513 0.241 Inf 0.021 0.9830
## white male - black male 0.35199 0.169 Inf 2.078 0.0377
## white male - other male 0.10799 0.419 Inf 0.258 0.7966
## white male - white female 0.09762 0.146 Inf 0.669 0.5037
## white male - latinx female 0.20448 0.266 Inf 0.769 0.4420
## white male - black female 0.25930 0.165 Inf 1.576 0.1151
## white male - other female -0.93826 0.487 Inf -1.925 0.0542
## latinx male - black male 0.34686 0.268 Inf 1.293 0.1959
## latinx male - other male 0.10286 0.468 Inf 0.220 0.8259
## latinx male - white female 0.09249 0.254 Inf 0.364 0.7158
## latinx male - latinx female 0.19935 0.338 Inf 0.591 0.5548
## latinx male - black female 0.25417 0.265 Inf 0.959 0.3378
## latinx male - other female -0.94339 0.530 Inf -1.781 0.0750
## black male - other male -0.24400 0.435 Inf -0.561 0.5749
## black male - white female -0.25437 0.187 Inf -1.358 0.1745
## black male - latinx female -0.14751 0.291 Inf -0.507 0.6119
## black male - black female -0.09269 0.202 Inf -0.458 0.6466
## black male - other female -1.29024 0.501 Inf -2.574 0.0101
## other male - white female -0.01037 0.427 Inf -0.024 0.9806
## other male - latinx female 0.09649 0.481 Inf 0.201 0.8410
## other male - black female 0.15131 0.433 Inf 0.349 0.7269
## other male - other female -1.04624 0.631 Inf -1.658 0.0973
## white female - latinx female 0.10686 0.278 Inf 0.385 0.7004
## white female - black female 0.16168 0.183 Inf 0.884 0.3769
## white female - other female -1.03588 0.494 Inf -2.098 0.0359
## latinx female - black female 0.05482 0.288 Inf 0.190 0.8490
## latinx female - other female -1.14274 0.542 Inf -2.110 0.0349
## black female - other female -1.19756 0.500 Inf -2.397 0.0165Sensitivity Analysis: Examining Variance with Loaded Models
NLSY TIPI Variance Models
# Model
nlsy_tipi_variance_model <- glm(ever_suspended ~
race * sex * extra_tipi +
race * sex * agree_tipi +
race * sex * neuro_tipi +
race * sex * consc_tipi +
race * sex * open_tipi,
family = binomial, data = nlsy)
summary(nlsy_tipi_variance_model)##
## Call:
## glm(formula = ever_suspended ~ race * sex * extra_tipi + race *
## sex * agree_tipi + race * sex * neuro_tipi + race * sex *
## consc_tipi + race * sex * open_tipi, family = binomial, data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2166 -0.6919 -0.5651 -0.3826 2.4296
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.700825 0.499180 -1.404 0.1603
## racelatinx -0.140417 1.317285 -0.107 0.9151
## raceblack 0.745732 0.622798 1.197 0.2312
## raceother -0.564152 0.977320 -0.577 0.5638
## sexFEMALE -0.944225 0.855414 -1.104 0.2697
## extra_tipi -0.025332 0.052790 -0.480 0.6313
## agree_tipi -0.070637 0.059839 -1.180 0.2378
## neuro_tipi -0.238215 0.055566 -4.287 1.81e-05 ***
## consc_tipi 0.044189 0.057278 0.771 0.4404
## open_tipi 0.107790 0.064158 1.680 0.0929 .
## racelatinx:sexFEMALE -1.001610 2.215681 -0.452 0.6512
## raceblack:sexFEMALE 0.061798 1.041654 0.059 0.9527
## raceother:sexFEMALE 0.254510 1.799388 0.141 0.8875
## racelatinx:extra_tipi -0.079287 0.139394 -0.569 0.5695
## raceblack:extra_tipi -0.027247 0.065272 -0.417 0.6764
## raceother:extra_tipi -0.051245 0.102742 -0.499 0.6179
## sexFEMALE:extra_tipi 0.078132 0.088414 0.884 0.3769
## racelatinx:agree_tipi 0.096885 0.169335 0.572 0.5672
## raceblack:agree_tipi 0.014848 0.074781 0.199 0.8426
## raceother:agree_tipi 0.023299 0.117754 0.198 0.8432
## sexFEMALE:agree_tipi -0.001393 0.105093 -0.013 0.9894
## racelatinx:neuro_tipi 0.225355 0.159601 1.412 0.1580
## raceblack:neuro_tipi 0.129938 0.069444 1.871 0.0613 .
## raceother:neuro_tipi 0.101752 0.108494 0.938 0.3483
## sexFEMALE:neuro_tipi 0.139342 0.090718 1.536 0.1245
## racelatinx:consc_tipi -0.168997 0.158927 -1.063 0.2876
## raceblack:consc_tipi 0.019410 0.073437 0.264 0.7915
## raceother:consc_tipi 0.096612 0.117620 0.821 0.4114
## sexFEMALE:consc_tipi 0.098662 0.099807 0.989 0.3229
## racelatinx:open_tipi 0.045672 0.174495 0.262 0.7935
## raceblack:open_tipi -0.097037 0.078510 -1.236 0.2165
## raceother:open_tipi 0.009425 0.119757 0.079 0.9373
## sexFEMALE:open_tipi -0.271278 0.108181 -2.508 0.0122 *
## racelatinx:sexFEMALE:extra_tipi 0.295793 0.271166 1.091 0.2754
## raceblack:sexFEMALE:extra_tipi -0.052968 0.107586 -0.492 0.6225
## raceother:sexFEMALE:extra_tipi -0.038797 0.189656 -0.205 0.8379
## racelatinx:sexFEMALE:agree_tipi 0.063550 0.295986 0.215 0.8300
## raceblack:sexFEMALE:agree_tipi 0.029202 0.127287 0.229 0.8185
## raceother:sexFEMALE:agree_tipi -0.012326 0.228996 -0.054 0.9571
## racelatinx:sexFEMALE:neuro_tipi -0.164312 0.275573 -0.596 0.5510
## raceblack:sexFEMALE:neuro_tipi -0.126193 0.111506 -1.132 0.2578
## raceother:sexFEMALE:neuro_tipi 0.110550 0.197683 0.559 0.5760
## racelatinx:sexFEMALE:consc_tipi -0.135260 0.267456 -0.506 0.6130
## raceblack:sexFEMALE:consc_tipi -0.120309 0.123754 -0.972 0.3310
## raceother:sexFEMALE:consc_tipi -0.446251 0.205836 -2.168 0.0302 *
## racelatinx:sexFEMALE:open_tipi 0.145645 0.290730 0.501 0.6164
## raceblack:sexFEMALE:open_tipi 0.241674 0.130323 1.854 0.0637 .
## raceother:sexFEMALE:open_tipi 0.274362 0.228513 1.201 0.2299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7329.5 on 7278 degrees of freedom
## Residual deviance: 6865.3 on 7231 degrees of freedom
## AIC: 6961.3
##
## Number of Fisher Scoring iterations: 5#### EXTRAVERSION
plot_model(nlsy_tipi_variance_model,
type = "pred",
terms = c("extra_tipi", "race", "sex"))
nlsy_extra_tipi_var_model <- emtrends(nlsy_tipi_variance_model,
pairwise ~ race*sex,
var = "extra_tipi",
adjust = "none")
nlsy_extra_tipi_var_model## $emtrends
## race sex extra_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.0253 0.0528 Inf -0.1288 0.0781
## latinx MALE -0.1046 0.1290 Inf -0.3575 0.1482
## black MALE -0.0526 0.0384 Inf -0.1278 0.0227
## other MALE -0.0766 0.0881 Inf -0.2493 0.0962
## white FEMALE 0.0528 0.0709 Inf -0.0862 0.1918
## latinx FEMALE 0.2693 0.2215 Inf -0.1649 0.7035
## black FEMALE -0.0274 0.0478 Inf -0.1211 0.0663
## other FEMALE -0.0372 0.1428 Inf -0.3171 0.2426
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.07929 0.1394 Inf 0.569 0.5695
## white MALE - black MALE 0.02725 0.0653 Inf 0.417 0.6764
## white MALE - other MALE 0.05124 0.1027 Inf 0.499 0.6179
## white MALE - white FEMALE -0.07813 0.0884 Inf -0.884 0.3769
## white MALE - latinx FEMALE -0.29464 0.2277 Inf -1.294 0.1957
## white MALE - black FEMALE 0.00208 0.0712 Inf 0.029 0.9767
## white MALE - other FEMALE 0.01191 0.1522 Inf 0.078 0.9376
## latinx MALE - black MALE -0.05204 0.1346 Inf -0.387 0.6990
## latinx MALE - other MALE -0.02804 0.1562 Inf -0.179 0.8576
## latinx MALE - white FEMALE -0.15742 0.1472 Inf -1.069 0.2850
## latinx MALE - latinx FEMALE -0.37393 0.2563 Inf -1.459 0.1447
## latinx MALE - black FEMALE -0.07720 0.1376 Inf -0.561 0.5747
## latinx MALE - other FEMALE -0.06738 0.1924 Inf -0.350 0.7262
## black MALE - other MALE 0.02400 0.0961 Inf 0.250 0.8029
## black MALE - white FEMALE -0.10538 0.0806 Inf -1.307 0.1913
## black MALE - latinx FEMALE -0.32188 0.2248 Inf -1.432 0.1522
## black MALE - black FEMALE -0.02516 0.0613 Inf -0.411 0.6814
## black MALE - other FEMALE -0.01534 0.1478 Inf -0.104 0.9174
## other MALE - white FEMALE -0.12938 0.1131 Inf -1.144 0.2528
## other MALE - latinx FEMALE -0.34588 0.2384 Inf -1.451 0.1468
## other MALE - black FEMALE -0.04916 0.1003 Inf -0.490 0.6239
## other MALE - other FEMALE -0.03933 0.1678 Inf -0.234 0.8146
## white FEMALE - latinx FEMALE -0.21651 0.2326 Inf -0.931 0.3519
## white FEMALE - black FEMALE 0.08022 0.0855 Inf 0.938 0.3483
## white FEMALE - other FEMALE 0.09004 0.1594 Inf 0.565 0.5722
## latinx FEMALE - black FEMALE 0.29672 0.2266 Inf 1.309 0.1904
## latinx FEMALE - other FEMALE 0.30655 0.2635 Inf 1.163 0.2448
## black FEMALE - other FEMALE 0.00983 0.1506 Inf 0.065 0.9480#### AGREEABLENESS
plot_model(nlsy_tipi_variance_model,
type = "pred",
terms = c("agree_tipi", "race", "sex"))
nlsy_agree_tipi_var_model <- emtrends(nlsy_tipi_variance_model,
pairwise ~ race*sex,
var = "agree_tipi",
adjust = "none")
nlsy_agree_tipi_var_model## $emtrends
## race sex agree_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.0706 0.0598 Inf -0.188 0.0466
## latinx MALE 0.0262 0.1584 Inf -0.284 0.3367
## black MALE -0.0558 0.0448 Inf -0.144 0.0321
## other MALE -0.0473 0.1014 Inf -0.246 0.1514
## white FEMALE -0.0720 0.0864 Inf -0.241 0.0973
## latinx FEMALE 0.0884 0.2269 Inf -0.356 0.5331
## black FEMALE -0.0280 0.0561 Inf -0.138 0.0820
## other FEMALE -0.0611 0.1764 Inf -0.407 0.2846
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.09688 0.1693 Inf -0.572 0.5672
## white MALE - black MALE -0.01485 0.0748 Inf -0.199 0.8426
## white MALE - other MALE -0.02330 0.1178 Inf -0.198 0.8432
## white MALE - white FEMALE 0.00139 0.1051 Inf 0.013 0.9894
## white MALE - latinx FEMALE -0.15904 0.2346 Inf -0.678 0.4979
## white MALE - black FEMALE -0.04266 0.0820 Inf -0.520 0.6030
## white MALE - other FEMALE -0.00958 0.1863 Inf -0.051 0.9590
## latinx MALE - black MALE 0.08204 0.1646 Inf 0.498 0.6183
## latinx MALE - other MALE 0.07359 0.1881 Inf 0.391 0.6956
## latinx MALE - white FEMALE 0.09828 0.1804 Inf 0.545 0.5860
## latinx MALE - latinx FEMALE -0.06216 0.2767 Inf -0.225 0.8223
## latinx MALE - black FEMALE 0.05423 0.1680 Inf 0.323 0.7469
## latinx MALE - other FEMALE 0.08730 0.2371 Inf 0.368 0.7127
## black MALE - other MALE -0.00845 0.1109 Inf -0.076 0.9393
## black MALE - white FEMALE 0.01624 0.0973 Inf 0.167 0.8675
## black MALE - latinx FEMALE -0.14419 0.2313 Inf -0.624 0.5329
## black MALE - black FEMALE -0.02781 0.0718 Inf -0.387 0.6986
## black MALE - other FEMALE 0.00527 0.1820 Inf 0.029 0.9769
## other MALE - white FEMALE 0.02469 0.1332 Inf 0.185 0.8530
## other MALE - latinx FEMALE -0.13574 0.2485 Inf -0.546 0.5849
## other MALE - black FEMALE -0.01936 0.1159 Inf -0.167 0.8673
## other MALE - other FEMALE 0.01372 0.2035 Inf 0.067 0.9462
## white FEMALE - latinx FEMALE -0.16043 0.2428 Inf -0.661 0.5087
## white FEMALE - black FEMALE -0.04405 0.1030 Inf -0.428 0.6689
## white FEMALE - other FEMALE -0.01097 0.1964 Inf -0.056 0.9554
## latinx FEMALE - black FEMALE 0.11638 0.2337 Inf 0.498 0.6185
## latinx FEMALE - other FEMALE 0.14946 0.2874 Inf 0.520 0.6030
## black FEMALE - other FEMALE 0.03308 0.1851 Inf 0.179 0.8582#### EMOTIONAL STABILITY
plot_model(nlsy_tipi_variance_model,
type = "pred",
terms = c("neuro_tipi", "race", "sex"))
nlsy_neuro_tipi_var_model <- emtrends(nlsy_tipi_variance_model,
pairwise ~ race*sex,
var = "neuro_tipi",
adjust = "none")
nlsy_neuro_tipi_var_model## $emtrends
## race sex neuro_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.2382 0.0556 Inf -0.347 -0.12931
## latinx MALE -0.0129 0.1496 Inf -0.306 0.28038
## black MALE -0.1083 0.0417 Inf -0.190 -0.02664
## other MALE -0.1365 0.0932 Inf -0.319 0.04617
## white FEMALE -0.0989 0.0717 Inf -0.239 0.04168
## latinx FEMALE -0.0378 0.2129 Inf -0.455 0.37944
## black FEMALE -0.0951 0.0497 Inf -0.193 0.00226
## other FEMALE 0.1134 0.1489 Inf -0.178 0.40523
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.22536 0.1596 Inf -1.412 0.1580
## white MALE - black MALE -0.12994 0.0694 Inf -1.871 0.0613
## white MALE - other MALE -0.10175 0.1085 Inf -0.938 0.3483
## white MALE - white FEMALE -0.13934 0.0907 Inf -1.536 0.1245
## white MALE - latinx FEMALE -0.20039 0.2200 Inf -0.911 0.3624
## white MALE - black FEMALE -0.14309 0.0745 Inf -1.920 0.0549
## white MALE - other FEMALE -0.35164 0.1589 Inf -2.213 0.0269
## latinx MALE - black MALE 0.09542 0.1553 Inf 0.614 0.5390
## latinx MALE - other MALE 0.12360 0.1763 Inf 0.701 0.4831
## latinx MALE - white FEMALE 0.08601 0.1659 Inf 0.518 0.6042
## latinx MALE - latinx FEMALE 0.02497 0.2602 Inf 0.096 0.9236
## latinx MALE - black FEMALE 0.08227 0.1577 Inf 0.522 0.6018
## latinx MALE - other FEMALE -0.12629 0.2111 Inf -0.598 0.5496
## black MALE - other MALE 0.02819 0.1021 Inf 0.276 0.7824
## black MALE - white FEMALE -0.00940 0.0829 Inf -0.113 0.9097
## black MALE - latinx FEMALE -0.07045 0.2169 Inf -0.325 0.7454
## black MALE - black FEMALE -0.01315 0.0648 Inf -0.203 0.8393
## black MALE - other FEMALE -0.22171 0.1546 Inf -1.434 0.1515
## other MALE - white FEMALE -0.03759 0.1176 Inf -0.320 0.7492
## other MALE - latinx FEMALE -0.09863 0.2324 Inf -0.424 0.6713
## other MALE - black FEMALE -0.04133 0.1056 Inf -0.391 0.6955
## other MALE - other FEMALE -0.24989 0.1756 Inf -1.423 0.1548
## white FEMALE - latinx FEMALE -0.06104 0.2247 Inf -0.272 0.7858
## white FEMALE - black FEMALE -0.00374 0.0872 Inf -0.043 0.9658
## white FEMALE - other FEMALE -0.21230 0.1653 Inf -1.285 0.1989
## latinx FEMALE - black FEMALE 0.05730 0.2186 Inf 0.262 0.7932
## latinx FEMALE - other FEMALE -0.15126 0.2598 Inf -0.582 0.5604
## black FEMALE - other FEMALE -0.20856 0.1570 Inf -1.329 0.1839#### CONSCIENTIOUSNESS
plot_model(nlsy_tipi_variance_model,
type = "pred",
terms = c("consc_tipi", "race", "sex"))
nlsy_consc_var_model <- emtrends(nlsy_tipi_variance_model,
pairwise ~ race*sex,
var = "consc_tipi",
adjust = "none")
nlsy_consc_var_model## $emtrends
## race sex consc_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE 0.0442 0.0573 Inf -0.0681 0.156
## latinx MALE -0.1248 0.1482 Inf -0.4154 0.166
## black MALE 0.0636 0.0460 Inf -0.0265 0.154
## other MALE 0.1408 0.1027 Inf -0.0605 0.342
## white FEMALE 0.1429 0.0817 Inf -0.0173 0.303
## latinx FEMALE -0.1614 0.1990 Inf -0.5514 0.229
## black FEMALE 0.0420 0.0569 Inf -0.0696 0.154
## other FEMALE -0.2068 0.1478 Inf -0.4965 0.083
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.16900 0.1589 Inf 1.063 0.2876
## white MALE - black MALE -0.01941 0.0734 Inf -0.264 0.7915
## white MALE - other MALE -0.09661 0.1176 Inf -0.821 0.4114
## white MALE - white FEMALE -0.09866 0.0998 Inf -0.989 0.3229
## white MALE - latinx FEMALE 0.20559 0.2071 Inf 0.993 0.3208
## white MALE - black FEMALE 0.00224 0.0808 Inf 0.028 0.9779
## white MALE - other FEMALE 0.25098 0.1585 Inf 1.583 0.1134
## latinx MALE - black MALE -0.18841 0.1552 Inf -1.214 0.2248
## latinx MALE - other MALE -0.26561 0.1804 Inf -1.473 0.1408
## latinx MALE - white FEMALE -0.26766 0.1693 Inf -1.581 0.1139
## latinx MALE - latinx FEMALE 0.03660 0.2481 Inf 0.147 0.8827
## latinx MALE - black FEMALE -0.16676 0.1588 Inf -1.050 0.2937
## latinx MALE - other FEMALE 0.08198 0.2094 Inf 0.392 0.6954
## black MALE - other MALE -0.07720 0.1125 Inf -0.686 0.4927
## black MALE - white FEMALE -0.07925 0.0938 Inf -0.845 0.3980
## black MALE - latinx FEMALE 0.22500 0.2042 Inf 1.102 0.2706
## black MALE - black FEMALE 0.02165 0.0732 Inf 0.296 0.7673
## black MALE - other FEMALE 0.27039 0.1548 Inf 1.747 0.0807
## other MALE - white FEMALE -0.00205 0.1313 Inf -0.016 0.9875
## other MALE - latinx FEMALE 0.30221 0.2239 Inf 1.350 0.1772
## other MALE - black FEMALE 0.09885 0.1175 Inf 0.842 0.4000
## other MALE - other FEMALE 0.34759 0.1800 Inf 1.931 0.0535
## white FEMALE - latinx FEMALE 0.30426 0.2151 Inf 1.414 0.1572
## white FEMALE - black FEMALE 0.10090 0.0996 Inf 1.013 0.3111
## white FEMALE - other FEMALE 0.34964 0.1689 Inf 2.070 0.0385
## latinx FEMALE - black FEMALE -0.20336 0.2070 Inf -0.983 0.3258
## latinx FEMALE - other FEMALE 0.04538 0.2479 Inf 0.183 0.8547
## black FEMALE - other FEMALE 0.24874 0.1584 Inf 1.570 0.1164#### OPENNESS
plot_model(nlsy_tipi_variance_model,
type = "pred",
terms = c("open_tipi", "race", "sex"))
nlsy_open_tipi_var_model <- emtrends(nlsy_tipi_variance_model,
pairwise ~ race*sex,
var = "open_tipi",
adjust = "none")
nlsy_open_tipi_var_model## $emtrends
## race sex open_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE 0.1078 0.0642 Inf -0.0180 0.23354
## latinx MALE 0.1535 0.1623 Inf -0.1646 0.47151
## black MALE 0.0108 0.0453 Inf -0.0779 0.09944
## other MALE 0.1172 0.1011 Inf -0.0810 0.31541
## white FEMALE -0.1635 0.0871 Inf -0.3342 0.00723
## latinx FEMALE 0.0278 0.2156 Inf -0.3948 0.45042
## black FEMALE -0.0189 0.0569 Inf -0.1303 0.09260
## other FEMALE 0.1203 0.1740 Inf -0.2208 0.46141
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.04567 0.1745 Inf -0.262 0.7935
## white MALE - black MALE 0.09704 0.0785 Inf 1.236 0.2165
## white MALE - other MALE -0.00942 0.1198 Inf -0.079 0.9373
## white MALE - white FEMALE 0.27128 0.1082 Inf 2.508 0.0122
## white MALE - latinx FEMALE 0.07996 0.2250 Inf 0.355 0.7222
## white MALE - black FEMALE 0.12664 0.0857 Inf 1.477 0.1396
## white MALE - other FEMALE -0.01251 0.1855 Inf -0.067 0.9462
## latinx MALE - black MALE 0.14271 0.1685 Inf 0.847 0.3969
## latinx MALE - other MALE 0.03625 0.1912 Inf 0.190 0.8496
## latinx MALE - white FEMALE 0.31695 0.1842 Inf 1.721 0.0853
## latinx MALE - latinx FEMALE 0.12563 0.2699 Inf 0.466 0.6415
## latinx MALE - black FEMALE 0.17231 0.1719 Inf 1.002 0.3163
## latinx MALE - other FEMALE 0.03316 0.2380 Inf 0.139 0.8892
## black MALE - other MALE -0.10646 0.1108 Inf -0.961 0.3366
## black MALE - white FEMALE 0.17424 0.0982 Inf 1.775 0.0759
## black MALE - latinx FEMALE -0.01708 0.2203 Inf -0.078 0.9382
## black MALE - black FEMALE 0.02960 0.0727 Inf 0.407 0.6837
## black MALE - other FEMALE -0.10955 0.1798 Inf -0.609 0.5424
## other MALE - white FEMALE 0.28070 0.1335 Inf 2.103 0.0354
## other MALE - latinx FEMALE 0.08939 0.2381 Inf 0.375 0.7074
## other MALE - black FEMALE 0.13606 0.1160 Inf 1.173 0.2409
## other MALE - other FEMALE -0.00308 0.2013 Inf -0.015 0.9878
## white FEMALE - latinx FEMALE -0.19132 0.2325 Inf -0.823 0.4107
## white FEMALE - black FEMALE -0.14464 0.1040 Inf -1.390 0.1644
## white FEMALE - other FEMALE -0.28379 0.1946 Inf -1.458 0.1448
## latinx FEMALE - black FEMALE 0.04668 0.2230 Inf 0.209 0.8342
## latinx FEMALE - other FEMALE -0.09247 0.2771 Inf -0.334 0.7386
## black FEMALE - other FEMALE -0.13915 0.1831 Inf -0.760 0.4473NLSY MINI-IPIP Variance Models
## Model
nlsy_mini_ipip_variance_model <- glm(ever_suspended ~
race * sex * extrav_mi +
race * sex * agree_mi +
race * sex * neuro_mi +
race * sex * consc_mi +
race * sex * open_mi,
family = binomial, data = nlsy)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(nlsy_mini_ipip_variance_model)##
## Call:
## glm(formula = ever_suspended ~ race * sex * extrav_mi + race *
## sex * agree_mi + race * sex * neuro_mi + race * sex * consc_mi +
## race * sex * open_mi, family = binomial, data = nlsy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4162 -0.6786 -0.4837 -0.3215 2.6897
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.200e+00 1.215e+00 1.812 0.0700 .
## racelatinx 3.766e+00 5.285e+00 0.713 0.4761
## raceblack 5.500e-01 1.610e+00 0.342 0.7326
## raceother -2.238e+00 2.435e+00 -0.919 0.3579
## sexFEMALE -1.828e+00 2.032e+00 -0.900 0.3682
## extrav_mi 4.439e-02 1.816e-01 0.244 0.8069
## agree_mi -4.912e-01 2.376e-01 -2.068 0.0387 *
## neuro_mi -1.350e-01 2.196e-01 -0.615 0.5388
## consc_mi -1.722e-01 2.205e-01 -0.781 0.4349
## open_mi -3.471e-01 2.126e-01 -1.633 0.1025
## racelatinx:sexFEMALE 8.004e+01 5.091e+04 0.002 0.9987
## raceblack:sexFEMALE -1.387e+00 2.540e+00 -0.546 0.5849
## raceother:sexFEMALE 1.083e+00 4.358e+00 0.249 0.8037
## racelatinx:extrav_mi -2.966e-01 6.663e-01 -0.445 0.6562
## raceblack:extrav_mi -1.754e-01 2.374e-01 -0.739 0.4600
## raceother:extrav_mi 3.305e-01 3.570e-01 0.926 0.3545
## sexFEMALE:extrav_mi 7.669e-03 2.795e-01 0.027 0.9781
## racelatinx:agree_mi 4.470e-01 9.844e-01 0.454 0.6497
## raceblack:agree_mi 5.145e-01 3.005e-01 1.712 0.0869 .
## raceother:agree_mi 1.620e-01 4.384e-01 0.370 0.7117
## sexFEMALE:agree_mi 3.839e-01 3.871e-01 0.992 0.3213
## racelatinx:neuro_mi -8.407e-01 8.066e-01 -1.042 0.2973
## raceblack:neuro_mi -3.993e-01 2.907e-01 -1.374 0.1696
## raceother:neuro_mi -3.782e-01 4.563e-01 -0.829 0.4072
## sexFEMALE:neuro_mi -2.222e-01 3.324e-01 -0.668 0.5039
## racelatinx:consc_mi 6.044e-01 7.885e-01 0.767 0.4433
## raceblack:consc_mi -1.601e-01 2.895e-01 -0.553 0.5803
## raceother:consc_mi 5.994e-02 4.360e-01 0.137 0.8907
## sexFEMALE:consc_mi 1.216e-01 3.304e-01 0.368 0.7128
## racelatinx:open_mi -1.004e+00 1.126e+00 -0.891 0.3729
## raceblack:open_mi 4.290e-01 2.813e-01 1.525 0.1273
## raceother:open_mi 6.285e-01 4.014e-01 1.566 0.1174
## sexFEMALE:open_mi 9.014e-02 3.479e-01 0.259 0.7955
## racelatinx:sexFEMALE:extrav_mi 1.821e+01 4.336e+03 0.004 0.9967
## raceblack:sexFEMALE:extrav_mi 1.970e-01 3.606e-01 0.546 0.5849
## raceother:sexFEMALE:extrav_mi -8.811e-01 6.141e-01 -1.435 0.1514
## racelatinx:sexFEMALE:agree_mi -2.857e+01 8.829e+03 -0.003 0.9974
## raceblack:sexFEMALE:agree_mi -4.840e-01 4.789e-01 -1.011 0.3121
## raceother:sexFEMALE:agree_mi -9.721e-01 7.861e-01 -1.237 0.2162
## racelatinx:sexFEMALE:neuro_mi -8.355e-01 8.470e+03 0.000 0.9999
## raceblack:sexFEMALE:neuro_mi 4.407e-01 4.268e-01 1.033 0.3018
## raceother:sexFEMALE:neuro_mi -2.330e-01 8.984e-01 -0.259 0.7954
## racelatinx:sexFEMALE:consc_mi -2.570e+00 2.206e+03 -0.001 0.9991
## raceblack:sexFEMALE:consc_mi 4.058e-01 4.389e-01 0.925 0.3551
## raceother:sexFEMALE:consc_mi 1.161e+00 8.397e-01 1.383 0.1667
## racelatinx:sexFEMALE:open_mi -1.292e+01 1.316e+04 -0.001 0.9992
## raceblack:sexFEMALE:open_mi -2.981e-01 4.465e-01 -0.668 0.5044
## raceother:sexFEMALE:open_mi 3.227e-01 7.971e-01 0.405 0.6856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1289.0 on 1310 degrees of freedom
## Residual deviance: 1145.4 on 1263 degrees of freedom
## (5968 observations deleted due to missingness)
## AIC: 1241.4
##
## Number of Fisher Scoring iterations: 16#### EXTRAVERSION
plot_model(nlsy_mini_ipip_variance_model,
type = "pred",
terms = c("extrav_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="extrav_mi [all]"` to get smooth plots.
nlsy_extra_mini_ipip_var_model <- emtrends(nlsy_mini_ipip_variance_model,
pairwise ~ race*sex,
var = "extrav_mi",
adjust = "none")
nlsy_extra_mini_ipip_var_model## $emtrends
## race sex extrav_mi.trend SE df asymp.LCL asymp.UCL
## white MALE 0.0444 0.182 Inf -0.312 0.400
## latinx MALE -0.2522 0.641 Inf -1.509 1.004
## black MALE -0.1310 0.153 Inf -0.431 0.169
## other MALE 0.3749 0.307 Inf -0.227 0.977
## white FEMALE 0.0521 0.212 Inf -0.364 0.468
## latinx FEMALE 17.9609 4336.475 Inf -8481.373 8517.295
## black FEMALE 0.0736 0.169 Inf -0.258 0.405
## other FEMALE -0.4986 0.452 Inf -1.385 0.388
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.29662 0.666 Inf 0.445 0.6562
## white MALE - black MALE 0.17544 0.237 Inf 0.739 0.4600
## white MALE - other MALE -0.33051 0.357 Inf -0.926 0.3545
## white MALE - white FEMALE -0.00767 0.280 Inf -0.027 0.9781
## white MALE - latinx FEMALE -17.91651 4336.475 Inf -0.004 0.9967
## white MALE - black FEMALE -0.02923 0.248 Inf -0.118 0.9062
## white MALE - other FEMALE 0.54297 0.487 Inf 1.114 0.2653
## latinx MALE - black MALE -0.12118 0.659 Inf -0.184 0.8541
## latinx MALE - other MALE -0.62712 0.711 Inf -0.882 0.3777
## latinx MALE - white FEMALE -0.30429 0.675 Inf -0.451 0.6523
## latinx MALE - latinx FEMALE -18.21313 4336.475 Inf -0.004 0.9966
## latinx MALE - black FEMALE -0.32585 0.663 Inf -0.491 0.6231
## latinx MALE - other FEMALE 0.24635 0.785 Inf 0.314 0.7535
## black MALE - other MALE -0.50594 0.343 Inf -1.474 0.1405
## black MALE - white FEMALE -0.18310 0.262 Inf -0.700 0.4842
## black MALE - latinx FEMALE -18.09195 4336.475 Inf -0.004 0.9967
## black MALE - black FEMALE -0.20467 0.228 Inf -0.898 0.3691
## black MALE - other FEMALE 0.36753 0.477 Inf 0.770 0.4415
## other MALE - white FEMALE 0.32284 0.374 Inf 0.864 0.3875
## other MALE - latinx FEMALE -17.58601 4336.475 Inf -0.004 0.9968
## other MALE - black FEMALE 0.30128 0.351 Inf 0.859 0.3903
## other MALE - other FEMALE 0.87347 0.547 Inf 1.597 0.1102
## white FEMALE - latinx FEMALE -17.90884 4336.475 Inf -0.004 0.9967
## white FEMALE - black FEMALE -0.02156 0.271 Inf -0.079 0.9367
## white FEMALE - other FEMALE 0.55063 0.500 Inf 1.102 0.2705
## latinx FEMALE - black FEMALE 17.88728 4336.475 Inf 0.004 0.9967
## latinx FEMALE - other FEMALE 18.45948 4336.475 Inf 0.004 0.9966
## black FEMALE - other FEMALE 0.57219 0.483 Inf 1.185 0.2360#### AGREEABLENESS
plot_model(nlsy_mini_ipip_variance_model,
type = "pred",
terms = c("agree_mi", "race", "sex"))
nlsy_agree_mini_ipip_var_model <- emtrends(nlsy_mini_ipip_variance_model,
pairwise ~ race*sex,
var = "agree_mi",
adjust = "none")
nlsy_agree_mini_ipip_var_model## $emtrends
## race sex agree_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.4912 0.238 Inf -9.57e-01 -0.026
## latinx MALE -0.0442 0.955 Inf -1.92e+00 1.828
## black MALE 0.0232 0.184 Inf -3.37e-01 0.384
## other MALE -0.3292 0.368 Inf -1.05e+00 0.393
## white FEMALE -0.1073 0.306 Inf -7.06e-01 0.492
## latinx FEMALE -28.2274 8828.771 Inf -1.73e+04 17275.845
## black FEMALE -0.0769 0.214 Inf -4.96e-01 0.342
## other FEMALE -0.9174 0.577 Inf -2.05e+00 0.213
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.4470 0.984 Inf -0.454 0.6497
## white MALE - black MALE -0.5145 0.300 Inf -1.712 0.0869
## white MALE - other MALE -0.1620 0.438 Inf -0.370 0.7117
## white MALE - white FEMALE -0.3839 0.387 Inf -0.992 0.3213
## white MALE - latinx FEMALE 27.7361 8828.771 Inf 0.003 0.9975
## white MALE - black FEMALE -0.4143 0.320 Inf -1.296 0.1948
## white MALE - other FEMALE 0.4262 0.624 Inf 0.683 0.4943
## latinx MALE - black MALE -0.0674 0.973 Inf -0.069 0.9448
## latinx MALE - other MALE 0.2850 1.024 Inf 0.278 0.7807
## latinx MALE - white FEMALE 0.0631 1.003 Inf 0.063 0.9498
## latinx MALE - latinx FEMALE 28.1832 8828.771 Inf 0.003 0.9975
## latinx MALE - black FEMALE 0.0327 0.979 Inf 0.033 0.9733
## latinx MALE - other FEMALE 0.8732 1.116 Inf 0.783 0.4339
## black MALE - other MALE 0.3524 0.412 Inf 0.856 0.3921
## black MALE - white FEMALE 0.1306 0.357 Inf 0.366 0.7143
## black MALE - latinx FEMALE 28.2506 8828.771 Inf 0.003 0.9974
## black MALE - black FEMALE 0.1001 0.282 Inf 0.355 0.7225
## black MALE - other FEMALE 0.9406 0.605 Inf 1.554 0.1201
## other MALE - white FEMALE -0.2219 0.479 Inf -0.464 0.6430
## other MALE - latinx FEMALE 27.8982 8828.771 Inf 0.003 0.9975
## other MALE - black FEMALE -0.2523 0.426 Inf -0.592 0.5536
## other MALE - other FEMALE 0.5882 0.684 Inf 0.860 0.3899
## white FEMALE - latinx FEMALE 28.1200 8828.771 Inf 0.003 0.9975
## white FEMALE - black FEMALE -0.0304 0.373 Inf -0.082 0.9350
## white FEMALE - other FEMALE 0.8101 0.652 Inf 1.242 0.2144
## latinx FEMALE - black FEMALE -28.1505 8828.771 Inf -0.003 0.9975
## latinx FEMALE - other FEMALE -27.3100 8828.771 Inf -0.003 0.9975
## black FEMALE - other FEMALE 0.8405 0.615 Inf 1.367 0.1716#### EMOTIONAL STABILITY
plot_model(nlsy_mini_ipip_variance_model,
type = "pred",
terms = c("neuro_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="neuro_mi [all]"` to get smooth plots.
nlsy_neuro_mini_ipip_var_model <- emtrends(nlsy_mini_ipip_variance_model,
pairwise ~ race*sex,
var = "neuro_mi",
adjust = "none")
nlsy_neuro_mini_ipip_var_model## $emtrends
## race sex neuro_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.135 0.220 Inf -5.65e-01 0.295
## latinx MALE -0.976 0.776 Inf -2.50e+00 0.545
## black MALE -0.534 0.190 Inf -9.08e-01 -0.161
## other MALE -0.513 0.400 Inf -1.30e+00 0.271
## white FEMALE -0.357 0.250 Inf -8.46e-01 0.132
## latinx FEMALE -2.033 8470.119 Inf -1.66e+04 16599.095
## black FEMALE -0.316 0.188 Inf -6.84e-01 0.053
## other FEMALE -0.968 0.733 Inf -2.40e+00 0.467
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 0.84071 0.807 Inf 1.042 0.2973
## white MALE - black MALE 0.39932 0.291 Inf 1.374 0.1696
## white MALE - other MALE 0.37820 0.456 Inf 0.829 0.4072
## white MALE - white FEMALE 0.22216 0.332 Inf 0.668 0.5039
## white MALE - latinx FEMALE 1.89837 8470.119 Inf 0.000 0.9998
## white MALE - black FEMALE 0.18078 0.289 Inf 0.625 0.5317
## white MALE - other FEMALE 0.83331 0.765 Inf 1.090 0.2758
## latinx MALE - black MALE -0.44139 0.799 Inf -0.552 0.5807
## latinx MALE - other MALE -0.46252 0.873 Inf -0.530 0.5963
## latinx MALE - white FEMALE -0.61855 0.815 Inf -0.759 0.4480
## latinx MALE - latinx FEMALE 1.05766 8470.119 Inf 0.000 0.9999
## latinx MALE - black FEMALE -0.65993 0.799 Inf -0.826 0.4086
## latinx MALE - other FEMALE -0.00741 1.067 Inf -0.007 0.9945
## black MALE - other MALE -0.02113 0.443 Inf -0.048 0.9620
## black MALE - white FEMALE -0.17717 0.314 Inf -0.564 0.5725
## black MALE - latinx FEMALE 1.49904 8470.119 Inf 0.000 0.9999
## black MALE - black FEMALE -0.21854 0.268 Inf -0.817 0.4141
## black MALE - other FEMALE 0.43398 0.757 Inf 0.573 0.5664
## other MALE - white FEMALE -0.15604 0.471 Inf -0.331 0.7407
## other MALE - latinx FEMALE 1.52017 8470.119 Inf 0.000 0.9999
## other MALE - black FEMALE -0.19741 0.442 Inf -0.447 0.6551
## other MALE - other FEMALE 0.45511 0.835 Inf 0.545 0.5855
## white FEMALE - latinx FEMALE 1.67621 8470.119 Inf 0.000 0.9998
## white FEMALE - black FEMALE -0.04138 0.312 Inf -0.132 0.8946
## white FEMALE - other FEMALE 0.61115 0.774 Inf 0.790 0.4297
## latinx FEMALE - black FEMALE -1.71759 8470.119 Inf 0.000 0.9998
## latinx FEMALE - other FEMALE -1.06506 8470.119 Inf 0.000 0.9999
## black FEMALE - other FEMALE 0.65252 0.756 Inf 0.863 0.3882#### CONSCIENTIOUSNESS
plot_model(nlsy_mini_ipip_variance_model,
type = "pred",
terms = c("consc_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="consc_mi [all]"` to get smooth plots.
nlsy_consc_mini_ipip_var_model <- emtrends(nlsy_mini_ipip_variance_model,
pairwise ~ race*sex,
var = "consc_mi",
adjust = "none")
nlsy_consc_mini_ipip_var_model## $emtrends
## race sex consc_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.1722 0.221 Inf -0.604 0.260
## latinx MALE 0.4322 0.757 Inf -1.052 1.916
## black MALE -0.3323 0.188 Inf -0.700 0.035
## other MALE -0.1123 0.376 Inf -0.849 0.625
## white FEMALE -0.0506 0.246 Inf -0.533 0.432
## latinx FEMALE -2.0159 2205.809 Inf -4325.323 4321.291
## black FEMALE 0.1952 0.220 Inf -0.235 0.626
## other FEMALE 1.1707 0.674 Inf -0.151 2.492
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE -0.6044 0.788 Inf -0.767 0.4433
## white MALE - black MALE 0.1601 0.289 Inf 0.553 0.5803
## white MALE - other MALE -0.0599 0.436 Inf -0.137 0.8907
## white MALE - white FEMALE -0.1216 0.330 Inf -0.368 0.7128
## white MALE - latinx FEMALE 1.8438 2205.809 Inf 0.001 0.9993
## white MALE - black FEMALE -0.3674 0.311 Inf -1.180 0.2379
## white MALE - other FEMALE -1.3429 0.709 Inf -1.893 0.0583
## latinx MALE - black MALE 0.7645 0.780 Inf 0.980 0.3270
## latinx MALE - other MALE 0.5445 0.845 Inf 0.644 0.5195
## latinx MALE - white FEMALE 0.4828 0.796 Inf 0.607 0.5442
## latinx MALE - latinx FEMALE 2.4482 2205.809 Inf 0.001 0.9991
## latinx MALE - black FEMALE 0.2371 0.788 Inf 0.301 0.7636
## latinx MALE - other FEMALE -0.7384 1.014 Inf -0.728 0.4663
## black MALE - other MALE -0.2200 0.420 Inf -0.523 0.6007
## black MALE - white FEMALE -0.2817 0.309 Inf -0.910 0.3626
## black MALE - latinx FEMALE 1.6837 2205.809 Inf 0.001 0.9994
## black MALE - black FEMALE -0.5274 0.289 Inf -1.826 0.0679
## black MALE - other FEMALE -1.5029 0.700 Inf -2.148 0.0317
## other MALE - white FEMALE -0.0617 0.449 Inf -0.137 0.8908
## other MALE - latinx FEMALE 1.9037 2205.809 Inf 0.001 0.9993
## other MALE - black FEMALE -0.3074 0.436 Inf -0.706 0.4803
## other MALE - other FEMALE -1.2829 0.772 Inf -1.662 0.0965
## white FEMALE - latinx FEMALE 1.9654 2205.809 Inf 0.001 0.9993
## white FEMALE - black FEMALE -0.2457 0.330 Inf -0.745 0.4562
## white FEMALE - other FEMALE -1.2212 0.718 Inf -1.702 0.0888
## latinx FEMALE - black FEMALE -2.2111 2205.809 Inf -0.001 0.9992
## latinx FEMALE - other FEMALE -3.1866 2205.809 Inf -0.001 0.9988
## black FEMALE - other FEMALE -0.9755 0.709 Inf -1.376 0.1689#### OPENNESS
plot_model(nlsy_mini_ipip_variance_model,
type = "pred",
terms = c("open_mi", "race", "sex"))## Data were 'prettified'. Consider using `terms="open_mi [all]"` to get smooth plots.
nlsy_open_mini_ipip_var_model <- emtrends(nlsy_mini_ipip_variance_model,
pairwise ~ race*sex,
var = "open_mi",
adjust = "none")
nlsy_open_mini_ipip_var_model## $emtrends
## race sex open_mi.trend SE df asymp.LCL asymp.UCL
## white MALE -0.3471 2.13e-01 Inf -7.64e-01 7.00e-02
## latinx MALE -1.3506 1.11e+00 Inf -3.52e+00 8.17e-01
## black MALE 0.0819 1.84e-01 Inf -2.79e-01 4.43e-01
## other MALE 0.2814 3.41e-01 Inf -3.86e-01 9.49e-01
## white FEMALE -0.2569 2.75e-01 Inf -7.97e-01 2.83e-01
## latinx FEMALE -14.1818 1.32e+04 Inf -2.58e+04 2.58e+04
## black FEMALE -0.1260 2.11e-01 Inf -5.39e-01 2.87e-01
## other FEMALE 0.6942 6.31e-01 Inf -5.43e-01 1.93e+00
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white MALE - latinx MALE 1.0035 1.13e+00 Inf 0.891 0.3729
## white MALE - black MALE -0.4290 2.81e-01 Inf -1.525 0.1273
## white MALE - other MALE -0.6285 4.01e-01 Inf -1.566 0.1174
## white MALE - white FEMALE -0.0901 3.48e-01 Inf -0.259 0.7955
## white MALE - latinx FEMALE 13.8347 1.32e+04 Inf 0.001 0.9992
## white MALE - black FEMALE -0.2211 2.99e-01 Inf -0.739 0.4602
## white MALE - other FEMALE -1.0413 6.66e-01 Inf -1.563 0.1180
## latinx MALE - black MALE -1.4325 1.12e+00 Inf -1.277 0.2014
## latinx MALE - other MALE -1.6320 1.16e+00 Inf -1.410 0.1585
## latinx MALE - white FEMALE -1.0937 1.14e+00 Inf -0.959 0.3373
## latinx MALE - latinx FEMALE 12.8312 1.32e+04 Inf 0.001 0.9992
## latinx MALE - black FEMALE -1.2246 1.13e+00 Inf -1.088 0.2768
## latinx MALE - other FEMALE -2.0448 1.27e+00 Inf -1.606 0.1084
## black MALE - other MALE -0.1995 3.87e-01 Inf -0.515 0.6064
## black MALE - white FEMALE 0.3388 3.31e-01 Inf 1.023 0.3065
## black MALE - latinx FEMALE 14.2637 1.32e+04 Inf 0.001 0.9991
## black MALE - black FEMALE 0.2079 2.80e-01 Inf 0.743 0.4577
## black MALE - other FEMALE -0.6123 6.58e-01 Inf -0.931 0.3518
## other MALE - white FEMALE 0.5383 4.38e-01 Inf 1.229 0.2190
## other MALE - latinx FEMALE 14.4632 1.32e+04 Inf 0.001 0.9991
## other MALE - black FEMALE 0.4074 4.00e-01 Inf 1.017 0.3090
## other MALE - other FEMALE -0.4128 7.17e-01 Inf -0.576 0.5649
## white FEMALE - latinx FEMALE 13.9249 1.32e+04 Inf 0.001 0.9992
## white FEMALE - black FEMALE -0.1309 3.47e-01 Inf -0.378 0.7058
## white FEMALE - other FEMALE -0.9511 6.89e-01 Inf -1.381 0.1672
## latinx FEMALE - black FEMALE -14.0558 1.32e+04 Inf -0.001 0.9991
## latinx FEMALE - other FEMALE -14.8760 1.32e+04 Inf -0.001 0.9991
## black FEMALE - other FEMALE -0.8202 6.65e-01 Inf -1.233 0.2177ADD Health Variance Models
# Model
add_variance_model <- glm(ever_suspended ~
race * sex * open +
race * sex * extrav +
race * sex * agree +
race * sex * neuro +
race * sex * consc,
family = binomial, data = add)
summary(add_variance_model)##
## Call:
## glm(formula = ever_suspended ~ race * sex * open + race * sex *
## extrav + race * sex * agree + race * sex * neuro + race *
## sex * consc, family = binomial, data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1150 -0.8149 -0.5526 0.9502 2.4436
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.534105 0.545230 2.814 0.004898 **
## racelatinx 1.563040 1.494553 1.046 0.295642
## raceblack 3.698543 1.066940 3.466 0.000527 ***
## raceother 1.067622 2.619316 0.408 0.683571
## sexfemale -0.092729 0.904044 -0.103 0.918303
## open -0.203483 0.093624 -2.173 0.029750 *
## extrav 0.315453 0.080228 3.932 8.43e-05 ***
## agree -0.233499 0.096423 -2.422 0.015452 *
## neuro -0.377964 0.088609 -4.266 1.99e-05 ***
## consc -0.120075 0.084321 -1.424 0.154442
## racelatinx:sexfemale -0.957378 2.181341 -0.439 0.660738
## raceblack:sexfemale -2.855353 1.518840 -1.880 0.060114 .
## raceother:sexfemale -0.879587 4.165254 -0.211 0.832753
## racelatinx:open 0.222531 0.269703 0.825 0.409319
## raceblack:open -0.015533 0.191006 -0.081 0.935184
## raceother:open -0.199071 0.502647 -0.396 0.692072
## sexfemale:open -0.034919 0.159736 -0.219 0.826956
## racelatinx:extrav 0.030248 0.213566 0.142 0.887371
## raceblack:extrav -0.211666 0.151373 -1.398 0.162020
## raceother:extrav 0.157187 0.389529 0.404 0.686557
## sexfemale:extrav -0.029903 0.129859 -0.230 0.817880
## racelatinx:agree -0.401345 0.262146 -1.531 0.125770
## raceblack:agree -0.340852 0.205863 -1.656 0.097779 .
## raceother:agree 0.434641 0.524189 0.829 0.407009
## sexfemale:agree -0.184625 0.172959 -1.067 0.285770
## racelatinx:neuro 0.266812 0.241584 1.104 0.269407
## raceblack:neuro -0.299751 0.181322 -1.653 0.098302 .
## raceother:neuro -0.616552 0.402167 -1.533 0.125258
## sexfemale:neuro -0.012486 0.135861 -0.092 0.926774
## racelatinx:consc -0.453582 0.260713 -1.740 0.081899 .
## raceblack:consc 0.070615 0.181890 0.388 0.697847
## raceother:consc -0.141198 0.384014 -0.368 0.713104
## sexfemale:consc -0.024428 0.132866 -0.184 0.854129
## racelatinx:sexfemale:open -0.454797 0.412206 -1.103 0.269886
## raceblack:sexfemale:open -0.110545 0.275569 -0.401 0.688307
## raceother:sexfemale:open 1.251880 0.776484 1.612 0.106909
## racelatinx:sexfemale:extrav 0.326969 0.334055 0.979 0.327685
## raceblack:sexfemale:extrav 0.097893 0.214879 0.456 0.648698
## raceother:sexfemale:extrav 0.483232 0.639103 0.756 0.449584
## racelatinx:sexfemale:agree 0.177343 0.417147 0.425 0.670740
## raceblack:sexfemale:agree 0.389196 0.296490 1.313 0.189292
## raceother:sexfemale:agree -1.245939 0.916599 -1.359 0.174049
## racelatinx:sexfemale:neuro -0.367998 0.343126 -1.072 0.283501
## raceblack:sexfemale:neuro 0.215836 0.245079 0.881 0.378492
## raceother:sexfemale:neuro 0.001508 0.642643 0.002 0.998127
## racelatinx:sexfemale:consc 0.621848 0.377954 1.645 0.099908 .
## raceblack:sexfemale:consc 0.247673 0.249143 0.994 0.320174
## raceother:sexfemale:consc -0.089460 0.634678 -0.141 0.887907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5324.2 on 5044 degrees of freedom
## AIC: 5420.2
##
## Number of Fisher Scoring iterations: 5#### EXTRAVERSION
plot_model(add_variance_model,
type = "pred",
terms = c("extrav", "race", "sex"))## Data were 'prettified'. Consider using `terms="extrav [all]"` to get smooth plots.
add_extra_variance_model <- emtrends(add_variance_model,
pairwise ~ race*sex,
var = "extrav",
adjust = "none")
add_extra_variance_model## $emtrends
## race sex extrav.trend SE df asymp.LCL asymp.UCL
## white male 0.315 0.0802 Inf 0.1582 0.473
## latinx male 0.346 0.1979 Inf -0.0422 0.734
## black male 0.104 0.1284 Inf -0.1478 0.355
## other male 0.473 0.3812 Inf -0.2745 1.220
## white female 0.286 0.1021 Inf 0.0854 0.486
## latinx female 0.643 0.2357 Inf 0.1808 1.105
## black female 0.172 0.1133 Inf -0.0502 0.394
## other female 0.926 0.4963 Inf -0.0467 1.899
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.0302 0.214 Inf -0.142 0.8874
## white male - black male 0.2117 0.151 Inf 1.398 0.1620
## white male - other male -0.1572 0.390 Inf -0.404 0.6866
## white male - white female 0.0299 0.130 Inf 0.230 0.8179
## white male - latinx female -0.3273 0.249 Inf -1.315 0.1886
## white male - black female 0.1437 0.139 Inf 1.035 0.3006
## white male - other female -0.6105 0.503 Inf -1.214 0.2246
## latinx male - black male 0.2419 0.236 Inf 1.025 0.3051
## latinx male - other male -0.1269 0.430 Inf -0.296 0.7676
## latinx male - white female 0.0602 0.223 Inf 0.270 0.7871
## latinx male - latinx female -0.2971 0.308 Inf -0.965 0.3345
## latinx male - black female 0.1739 0.228 Inf 0.763 0.4457
## latinx male - other female -0.5803 0.534 Inf -1.086 0.2775
## black male - other male -0.3689 0.402 Inf -0.917 0.3591
## black male - white female -0.1818 0.164 Inf -1.108 0.2678
## black male - latinx female -0.5390 0.268 Inf -2.008 0.0446
## black male - black female -0.0680 0.171 Inf -0.397 0.6913
## black male - other female -0.8222 0.513 Inf -1.604 0.1087
## other male - white female 0.1871 0.395 Inf 0.474 0.6354
## other male - latinx female -0.1701 0.448 Inf -0.380 0.7042
## other male - black female 0.3009 0.398 Inf 0.757 0.4493
## other male - other female -0.4533 0.626 Inf -0.724 0.4688
## white female - latinx female -0.3572 0.257 Inf -1.391 0.1643
## white female - black female 0.1138 0.153 Inf 0.746 0.4557
## white female - other female -0.6404 0.507 Inf -1.264 0.2062
## latinx female - black female 0.4710 0.262 Inf 1.801 0.0717
## latinx female - other female -0.2832 0.549 Inf -0.515 0.6062
## black female - other female -0.7542 0.509 Inf -1.482 0.1385#### AGREEABLENESS
plot_model(add_variance_model,
type = "pred",
terms = c("agree", "race", "sex"))## Data were 'prettified'. Consider using `terms="agree [all]"` to get smooth plots.
add_agree_variance_model <- emtrends(add_variance_model,
pairwise ~ race*sex,
var = "agree",
adjust = "none")
add_agree_variance_model## $emtrends
## race sex agree.trend SE df asymp.LCL asymp.UCL
## white male -0.233 0.0964 Inf -0.422 -0.0445
## latinx male -0.635 0.2438 Inf -1.113 -0.1571
## black male -0.574 0.1819 Inf -0.931 -0.2179
## other male 0.201 0.5152 Inf -0.809 1.2110
## white female -0.418 0.1436 Inf -0.700 -0.1367
## latinx female -0.642 0.2910 Inf -1.212 -0.0718
## black female -0.370 0.1578 Inf -0.679 -0.0604
## other female -1.229 0.7381 Inf -2.676 0.2172
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.40134 0.262 Inf 1.531 0.1258
## white male - black male 0.34085 0.206 Inf 1.656 0.0978
## white male - other male -0.43464 0.524 Inf -0.829 0.4070
## white male - white female 0.18462 0.173 Inf 1.067 0.2858
## white male - latinx female 0.40863 0.307 Inf 1.333 0.1825
## white male - black female 0.13628 0.185 Inf 0.737 0.4612
## white male - other female 0.99592 0.744 Inf 1.338 0.1809
## latinx male - black male -0.06049 0.304 Inf -0.199 0.8423
## latinx male - other male -0.83599 0.570 Inf -1.467 0.1425
## latinx male - white female -0.21672 0.283 Inf -0.766 0.4437
## latinx male - latinx female 0.00728 0.380 Inf 0.019 0.9847
## latinx male - black female -0.26506 0.290 Inf -0.913 0.3614
## latinx male - other female 0.59458 0.777 Inf 0.765 0.4443
## black male - other male -0.77549 0.546 Inf -1.419 0.1558
## black male - white female -0.15623 0.232 Inf -0.674 0.5002
## black male - latinx female 0.06777 0.343 Inf 0.198 0.8434
## black male - black female -0.20457 0.241 Inf -0.849 0.3956
## black male - other female 0.65507 0.760 Inf 0.862 0.3888
## other male - white female 0.61927 0.535 Inf 1.158 0.2470
## other male - latinx female 0.84327 0.592 Inf 1.425 0.1541
## other male - black female 0.57092 0.539 Inf 1.059 0.2894
## other male - other female 1.43056 0.900 Inf 1.589 0.1120
## white female - latinx female 0.22400 0.324 Inf 0.690 0.4900
## white female - black female -0.04834 0.213 Inf -0.227 0.8208
## white female - other female 0.81130 0.752 Inf 1.079 0.2806
## latinx female - black female -0.27235 0.331 Inf -0.823 0.4107
## latinx female - other female 0.58730 0.793 Inf 0.740 0.4591
## black female - other female 0.85964 0.755 Inf 1.139 0.2547#### EMOTIONAL STABILITY
plot_model(add_variance_model,
type = "pred",
terms = c("neuro", "race", "sex"))## Data were 'prettified'. Consider using `terms="neuro [all]"` to get smooth plots.
add_neuro_variance_model <- emtrends(add_variance_model,
pairwise ~ race*sex,
var = "neuro",
adjust = "none")
add_neuro_variance_model## $emtrends
## race sex neuro.trend SE df asymp.LCL asymp.UCL
## white male -0.378 0.0886 Inf -0.552 -0.2043
## latinx male -0.111 0.2247 Inf -0.552 0.3293
## black male -0.678 0.1582 Inf -0.988 -0.3677
## other male -0.995 0.3923 Inf -1.763 -0.2257
## white female -0.390 0.1030 Inf -0.592 -0.1886
## latinx female -0.492 0.2208 Inf -0.924 -0.0588
## black female -0.474 0.1288 Inf -0.727 -0.2220
## other female -1.005 0.4906 Inf -1.967 -0.0440
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.2668 0.242 Inf -1.104 0.2694
## white male - black male 0.2998 0.181 Inf 1.653 0.0983
## white male - other male 0.6166 0.402 Inf 1.533 0.1253
## white male - white female 0.0125 0.136 Inf 0.092 0.9268
## white male - latinx female 0.1137 0.238 Inf 0.478 0.6328
## white male - black female 0.0964 0.156 Inf 0.617 0.5374
## white male - other female 0.6275 0.498 Inf 1.259 0.2081
## latinx male - black male 0.5666 0.275 Inf 2.061 0.0393
## latinx male - other male 0.8834 0.452 Inf 1.954 0.0507
## latinx male - white female 0.2793 0.247 Inf 1.130 0.2586
## latinx male - latinx female 0.3805 0.315 Inf 1.208 0.2272
## latinx male - black female 0.3632 0.259 Inf 1.402 0.1608
## latinx male - other female 0.8943 0.540 Inf 1.657 0.0974
## black male - other male 0.3168 0.423 Inf 0.749 0.4539
## black male - white female -0.2873 0.189 Inf -1.522 0.1281
## black male - latinx female -0.1861 0.272 Inf -0.685 0.4933
## black male - black female -0.2033 0.204 Inf -0.997 0.3188
## black male - other female 0.3278 0.515 Inf 0.636 0.5248
## other male - white female -0.6041 0.406 Inf -1.489 0.1364
## other male - latinx female -0.5029 0.450 Inf -1.117 0.2640
## other male - black female -0.5201 0.413 Inf -1.260 0.2077
## other male - other female 0.0110 0.628 Inf 0.017 0.9861
## white female - latinx female 0.1012 0.244 Inf 0.415 0.6779
## white female - black female 0.0839 0.165 Inf 0.509 0.6108
## white female - other female 0.6150 0.501 Inf 1.227 0.2198
## latinx female - black female -0.0173 0.256 Inf -0.068 0.9461
## latinx female - other female 0.5139 0.538 Inf 0.955 0.3395
## black female - other female 0.5311 0.507 Inf 1.047 0.2950#### CONSCIENTIOUSNESS
plot_model(add_variance_model,
type = "pred",
terms = c("consc", "race", "sex"))
add_consc_variance_model <- emtrends(add_variance_model,
pairwise ~ race*sex,
var = "consc",
adjust = "none")
add_consc_variance_model## $emtrends
## race sex consc.trend SE df asymp.LCL asymp.UCL
## white male -0.1201 0.0843 Inf -0.2853 0.0452
## latinx male -0.5737 0.2467 Inf -1.0572 -0.0901
## black male -0.0495 0.1612 Inf -0.3653 0.2664
## other male -0.2613 0.3746 Inf -0.9956 0.4730
## white female -0.1445 0.1027 Inf -0.3458 0.0567
## latinx female 0.0238 0.2536 Inf -0.4734 0.5209
## black female 0.1738 0.1358 Inf -0.0924 0.4400
## other female -0.3752 0.4948 Inf -1.3449 0.5946
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male 0.4536 0.261 Inf 1.740 0.0819
## white male - black male -0.0706 0.182 Inf -0.388 0.6978
## white male - other male 0.1412 0.384 Inf 0.368 0.7131
## white male - white female 0.0244 0.133 Inf 0.184 0.8541
## white male - latinx female -0.1438 0.267 Inf -0.538 0.5905
## white male - black female -0.2939 0.160 Inf -1.838 0.0660
## white male - other female 0.2551 0.502 Inf 0.508 0.6113
## latinx male - black male -0.5242 0.295 Inf -1.779 0.0753
## latinx male - other male -0.3124 0.449 Inf -0.696 0.4862
## latinx male - white female -0.4292 0.267 Inf -1.606 0.1083
## latinx male - latinx female -0.5974 0.354 Inf -1.688 0.0913
## latinx male - black female -0.7474 0.282 Inf -2.654 0.0080
## latinx male - other female -0.1985 0.553 Inf -0.359 0.7196
## black male - other male 0.2118 0.408 Inf 0.519 0.6035
## black male - white female 0.0950 0.191 Inf 0.497 0.6189
## black male - latinx female -0.0732 0.301 Inf -0.244 0.8075
## black male - black female -0.2232 0.211 Inf -1.059 0.2895
## black male - other female 0.3257 0.520 Inf 0.626 0.5314
## other male - white female -0.1168 0.388 Inf -0.301 0.7637
## other male - latinx female -0.2850 0.452 Inf -0.630 0.5287
## other male - black female -0.4351 0.398 Inf -1.092 0.2749
## other male - other female 0.1139 0.621 Inf 0.184 0.8544
## white female - latinx female -0.1683 0.274 Inf -0.615 0.5386
## white female - black female -0.3183 0.170 Inf -1.869 0.0616
## white female - other female 0.2307 0.505 Inf 0.456 0.6481
## latinx female - black female -0.1500 0.288 Inf -0.521 0.6021
## latinx female - other female 0.3989 0.556 Inf 0.717 0.4731
## black female - other female 0.5489 0.513 Inf 1.070 0.2847#### OPENNESS
plot_model(add_variance_model,
type = "pred",
terms = c("open", "race", "sex"))## Data were 'prettified'. Consider using `terms="open [all]"` to get smooth plots.
add_open_variance_model <- emtrends(add_variance_model,
pairwise ~ race*sex,
var = "open",
adjust = "none")
add_open_variance_model## $emtrends
## race sex open.trend SE df asymp.LCL asymp.UCL
## white male -0.203 0.0936 Inf -0.387 -0.0200
## latinx male 0.019 0.2529 Inf -0.477 0.5148
## black male -0.219 0.1665 Inf -0.545 0.1073
## other male -0.403 0.4939 Inf -1.370 0.5654
## white female -0.238 0.1294 Inf -0.492 0.0153
## latinx female -0.471 0.2836 Inf -1.026 0.0852
## black female -0.364 0.1507 Inf -0.660 -0.0692
## other female 0.814 0.5775 Inf -0.317 1.9463
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.2225 0.270 Inf -0.825 0.4093
## white male - black male 0.0155 0.191 Inf 0.081 0.9352
## white male - other male 0.1991 0.503 Inf 0.396 0.6921
## white male - white female 0.0349 0.160 Inf 0.219 0.8270
## white male - latinx female 0.2672 0.299 Inf 0.895 0.3710
## white male - black female 0.1610 0.177 Inf 0.908 0.3641
## white male - other female -1.0179 0.585 Inf -1.740 0.0819
## latinx male - black male 0.2381 0.303 Inf 0.786 0.4318
## latinx male - other male 0.4216 0.555 Inf 0.760 0.4473
## latinx male - white female 0.2575 0.284 Inf 0.906 0.3649
## latinx male - latinx female 0.4897 0.380 Inf 1.289 0.1975
## latinx male - black female 0.3835 0.294 Inf 1.303 0.1927
## latinx male - other female -0.7954 0.630 Inf -1.262 0.2071
## black male - other male 0.1835 0.521 Inf 0.352 0.7247
## black male - white female 0.0194 0.211 Inf 0.092 0.9268
## black male - latinx female 0.2517 0.329 Inf 0.765 0.4441
## black male - black female 0.1455 0.225 Inf 0.648 0.5171
## black male - other female -1.0334 0.601 Inf -1.719 0.0855
## other male - white female -0.1642 0.511 Inf -0.322 0.7478
## other male - latinx female 0.0681 0.569 Inf 0.120 0.9048
## other male - black female -0.0381 0.516 Inf -0.074 0.9412
## other male - other female -1.2170 0.760 Inf -1.602 0.1093
## white female - latinx female 0.2323 0.312 Inf 0.745 0.4562
## white female - black female 0.1261 0.199 Inf 0.635 0.5256
## white female - other female -1.0528 0.592 Inf -1.779 0.0753
## latinx female - black female -0.1062 0.321 Inf -0.331 0.7409
## latinx female - other female -1.2851 0.643 Inf -1.997 0.0458
## black female - other female -1.1789 0.597 Inf -1.975 0.0482Exploratory (Add Health)
Anger
anger <- glm(ever_suspended ~ race * sex * anger,
family = binomial, data = add)
summary(anger)##
## Call:
## glm(formula = ever_suspended ~ race * sex * anger, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6192 -0.8329 -0.5524 1.0852 2.2896
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.34111 0.26039 1.310 0.190193
## racelatinx -0.22939 0.69760 -0.329 0.742290
## raceblack 1.56094 0.54295 2.875 0.004042 **
## raceother 0.39089 1.20052 0.326 0.744727
## sexfemale -1.54000 0.43156 -3.568 0.000359 ***
## anger -0.32569 0.07426 -4.386 1.15e-05 ***
## racelatinx:sexfemale 1.08398 1.03218 1.050 0.293631
## raceblack:sexfemale 0.28063 0.75077 0.374 0.708558
## raceother:sexfemale 0.78191 1.80185 0.434 0.664326
## racelatinx:anger 0.18115 0.19457 0.931 0.351815
## raceblack:anger -0.19164 0.15135 -1.266 0.205433
## raceother:anger -0.16598 0.33328 -0.498 0.618475
## sexfemale:anger 0.12438 0.12516 0.994 0.320326
## racelatinx:sexfemale:anger -0.28670 0.30034 -0.955 0.339798
## raceblack:sexfemale:anger -0.01695 0.21442 -0.079 0.936978
## raceother:sexfemale:anger -0.16314 0.53116 -0.307 0.758746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5491.5 on 5076 degrees of freedom
## AIC: 5523.5
##
## Number of Fisher Scoring iterations: 4plot_model(anger, type = "pred", terms = c("anger", "race", "sex"))## Data were 'prettified'. Consider using `terms="anger [all]"` to get smooth plots.
object1 <- emtrends(anger, pairwise ~ race*sex, var = "anger", adjust = "none")
object1## $emtrends
## race sex anger.trend SE df asymp.LCL asymp.UCL
## white male -0.326 0.0743 Inf -0.471 -0.18015
## latinx male -0.145 0.1798 Inf -0.497 0.20794
## black male -0.517 0.1319 Inf -0.776 -0.25885
## other male -0.492 0.3249 Inf -1.128 0.14513
## white female -0.201 0.1007 Inf -0.399 -0.00385
## latinx female -0.307 0.2054 Inf -0.709 0.09577
## black female -0.410 0.1137 Inf -0.633 -0.18714
## other female -0.530 0.4011 Inf -1.317 0.25578
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.1812 0.195 Inf -0.931 0.3518
## white male - black male 0.1916 0.151 Inf 1.266 0.2054
## white male - other male 0.1660 0.333 Inf 0.498 0.6185
## white male - white female -0.1244 0.125 Inf -0.994 0.3203
## white male - latinx female -0.0188 0.218 Inf -0.086 0.9313
## white male - black female 0.0842 0.136 Inf 0.620 0.5350
## white male - other female 0.2047 0.408 Inf 0.502 0.6158
## latinx male - black male 0.3728 0.223 Inf 1.672 0.0946
## latinx male - other male 0.3471 0.371 Inf 0.935 0.3499
## latinx male - white female 0.0568 0.206 Inf 0.275 0.7830
## latinx male - latinx female 0.1623 0.273 Inf 0.595 0.5522
## latinx male - black female 0.2654 0.213 Inf 1.247 0.2123
## latinx male - other female 0.3859 0.440 Inf 0.878 0.3800
## black male - other male -0.0257 0.351 Inf -0.073 0.9417
## black male - white female -0.3160 0.166 Inf -1.904 0.0569
## black male - latinx female -0.2105 0.244 Inf -0.862 0.3886
## black male - black female -0.1074 0.174 Inf -0.617 0.5372
## black male - other female 0.0131 0.422 Inf 0.031 0.9753
## other male - white female -0.2904 0.340 Inf -0.854 0.3933
## other male - latinx female -0.1848 0.384 Inf -0.481 0.6307
## other male - black female -0.0818 0.344 Inf -0.238 0.8122
## other male - other female 0.0388 0.516 Inf 0.075 0.9402
## white female - latinx female 0.1055 0.229 Inf 0.461 0.6446
## white female - black female 0.2086 0.152 Inf 1.373 0.1696
## white female - other female 0.3291 0.414 Inf 0.796 0.4262
## latinx female - black female 0.1031 0.235 Inf 0.439 0.6607
## latinx female - other female 0.2236 0.451 Inf 0.496 0.6198
## black female - other female 0.1205 0.417 Inf 0.289 0.7725Optimism
optimism <- glm(ever_suspended ~ race * sex * optimism,
family = binomial, data = add)
summary(optimism)##
## Call:
## glm(formula = ever_suspended ~ race * sex * optimism, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7305 -0.8187 -0.5543 1.0664 2.2373
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.87370 0.30526 2.862 0.00421 **
## racelatinx -0.11552 0.91399 -0.126 0.89942
## raceblack 1.67323 0.66074 2.532 0.01133 *
## raceother 0.56083 1.45098 0.387 0.69911
## sexfemale -0.94228 0.51191 -1.841 0.06566 .
## optimism -0.45777 0.08360 -5.476 4.36e-08 ***
## racelatinx:sexfemale 0.20026 1.36131 0.147 0.88305
## raceblack:sexfemale -0.79308 0.94180 -0.842 0.39974
## raceother:sexfemale -3.89260 2.47925 -1.570 0.11640
## racelatinx:optimism 0.14810 0.24327 0.609 0.54268
## raceblack:optimism -0.19371 0.17350 -1.117 0.26420
## raceother:optimism -0.22436 0.40031 -0.560 0.57516
## sexfemale:optimism -0.03561 0.14000 -0.254 0.79924
## racelatinx:sexfemale:optimism -0.02354 0.36542 -0.064 0.94863
## raceblack:sexfemale:optimism 0.27947 0.24893 1.123 0.26158
## raceother:sexfemale:optimism 1.15462 0.65755 1.756 0.07910 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5465.8 on 5076 degrees of freedom
## AIC: 5497.8
##
## Number of Fisher Scoring iterations: 4plot_model(optimism, type = "pred", terms = c("optimism", "race", "sex"))## Data were 'prettified'. Consider using `terms="optimism [all]"` to get smooth plots.
object1 <- emtrends(optimism, pairwise ~ race*sex, var = "optimism", adjust = "none")
object1## $emtrends
## race sex optimism.trend SE df asymp.LCL asymp.UCL
## white male -0.458 0.0836 Inf -0.622 -0.2939
## latinx male -0.310 0.2285 Inf -0.757 0.1381
## black male -0.651 0.1520 Inf -0.949 -0.3535
## other male -0.682 0.3915 Inf -1.449 0.0852
## white female -0.493 0.1123 Inf -0.713 -0.2733
## latinx female -0.369 0.2485 Inf -0.856 0.1182
## black female -0.408 0.1388 Inf -0.680 -0.1357
## other female 0.437 0.5094 Inf -0.562 1.4353
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.1481 0.243 Inf -0.609 0.5427
## white male - black male 0.1937 0.173 Inf 1.117 0.2642
## white male - other male 0.2244 0.400 Inf 0.560 0.5752
## white male - white female 0.0356 0.140 Inf 0.254 0.7992
## white male - latinx female -0.0889 0.262 Inf -0.339 0.7344
## white male - black female -0.0501 0.162 Inf -0.310 0.7569
## white male - other female -0.8947 0.516 Inf -1.733 0.0831
## latinx male - black male 0.3418 0.274 Inf 1.246 0.2129
## latinx male - other male 0.3725 0.453 Inf 0.822 0.4112
## latinx male - white female 0.1837 0.255 Inf 0.722 0.4705
## latinx male - latinx female 0.0591 0.338 Inf 0.175 0.8609
## latinx male - black female 0.0980 0.267 Inf 0.366 0.7140
## latinx male - other female -0.7466 0.558 Inf -1.337 0.1812
## black male - other male 0.0306 0.420 Inf 0.073 0.9418
## black male - white female -0.1581 0.189 Inf -0.837 0.4029
## black male - latinx female -0.2827 0.291 Inf -0.970 0.3318
## black male - black female -0.2439 0.206 Inf -1.185 0.2361
## black male - other female -1.0884 0.532 Inf -2.047 0.0406
## other male - white female -0.1888 0.407 Inf -0.463 0.6430
## other male - latinx female -0.3133 0.464 Inf -0.676 0.4992
## other male - black female -0.2745 0.415 Inf -0.661 0.5087
## other male - other female -1.1190 0.642 Inf -1.742 0.0816
## white female - latinx female -0.1246 0.273 Inf -0.457 0.6478
## white female - black female -0.0858 0.179 Inf -0.480 0.6310
## white female - other female -0.9303 0.522 Inf -1.783 0.0745
## latinx female - black female 0.0388 0.285 Inf 0.136 0.8915
## latinx female - other female -0.8057 0.567 Inf -1.422 0.1552
## black female - other female -0.8445 0.528 Inf -1.599 0.1097Anxiety
anxiety <- glm(ever_suspended ~ race * sex * anxiety,
family = binomial, data = add)
summary(anxiety)##
## Call:
## glm(formula = ever_suspended ~ race * sex * anxiety, family = binomial,
## data = add)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5986 -0.8759 -0.5340 1.1099 2.0380
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.10901 0.24217 -0.450 0.65262
## racelatinx 0.14657 0.66058 0.222 0.82441
## raceblack 1.45522 0.50355 2.890 0.00385 **
## raceother 0.76788 1.03729 0.740 0.45913
## sexfemale -1.75784 0.37057 -4.744 2.1e-06 ***
## anxiety -0.21707 0.07629 -2.845 0.00444 **
## racelatinx:sexfemale 0.80192 0.90740 0.884 0.37683
## raceblack:sexfemale -0.24258 0.66947 -0.362 0.71709
## raceother:sexfemale -0.40654 1.62267 -0.251 0.80217
## racelatinx:anxiety 0.07557 0.20958 0.361 0.71841
## raceblack:anxiety -0.17805 0.15393 -1.157 0.24739
## raceother:anxiety -0.33066 0.33307 -0.993 0.32083
## sexfemale:anxiety 0.21262 0.12719 1.672 0.09460 .
## racelatinx:sexfemale:anxiety -0.23186 0.30873 -0.751 0.45265
## raceblack:sexfemale:anxiety 0.15329 0.21806 0.703 0.48207
## raceother:sexfemale:anxiety 0.24949 0.54433 0.458 0.64671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5936.2 on 5091 degrees of freedom
## Residual deviance: 5529.7 on 5076 degrees of freedom
## AIC: 5561.7
##
## Number of Fisher Scoring iterations: 4plot_model(anxiety, type = "pred", terms = c("anxiety", "race", "sex"))## Data were 'prettified'. Consider using `terms="anxiety [all]"` to get smooth plots.
object1 <- emtrends(anxiety, pairwise ~ race*sex, var = "anxiety", adjust = "none")
object1## $emtrends
## race sex anxiety.trend SE df asymp.LCL asymp.UCL
## white male -0.21707 0.0763 Inf -0.367 -0.0675
## latinx male -0.14149 0.1952 Inf -0.524 0.2411
## black male -0.39512 0.1337 Inf -0.657 -0.1331
## other male -0.54772 0.3242 Inf -1.183 0.0877
## white female -0.00445 0.1018 Inf -0.204 0.1950
## latinx female -0.16074 0.2026 Inf -0.558 0.2363
## black female -0.02921 0.1162 Inf -0.257 0.1985
## other female -0.08562 0.4183 Inf -0.906 0.7343
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## white male - latinx male -0.0756 0.210 Inf -0.361 0.7184
## white male - black male 0.1781 0.154 Inf 1.157 0.2474
## white male - other male 0.3307 0.333 Inf 0.993 0.3208
## white male - white female -0.2126 0.127 Inf -1.672 0.0946
## white male - latinx female -0.0563 0.216 Inf -0.260 0.7947
## white male - black female -0.1879 0.139 Inf -1.352 0.1765
## white male - other female -0.1314 0.425 Inf -0.309 0.7572
## latinx male - black male 0.2536 0.237 Inf 1.072 0.2837
## latinx male - other male 0.4062 0.378 Inf 1.073 0.2831
## latinx male - white female -0.1370 0.220 Inf -0.623 0.5336
## latinx male - latinx female 0.0192 0.281 Inf 0.068 0.9455
## latinx male - black female -0.1123 0.227 Inf -0.494 0.6211
## latinx male - other female -0.0559 0.462 Inf -0.121 0.9037
## black male - other male 0.1526 0.351 Inf 0.435 0.6635
## black male - white female -0.3907 0.168 Inf -2.325 0.0201
## black male - latinx female -0.2344 0.243 Inf -0.966 0.3342
## black male - black female -0.3659 0.177 Inf -2.066 0.0388
## black male - other female -0.3095 0.439 Inf -0.705 0.4810
## other male - white female -0.5433 0.340 Inf -1.599 0.1099
## other male - latinx female -0.3870 0.382 Inf -1.012 0.3114
## other male - black female -0.5185 0.344 Inf -1.506 0.1322
## other male - other female -0.4621 0.529 Inf -0.873 0.3826
## white female - latinx female 0.1563 0.227 Inf 0.689 0.4906
## white female - black female 0.0248 0.154 Inf 0.160 0.8726
## white female - other female 0.0812 0.431 Inf 0.189 0.8505
## latinx female - black female -0.1315 0.234 Inf -0.563 0.5733
## latinx female - other female -0.0751 0.465 Inf -0.162 0.8716
## black female - other female 0.0564 0.434 Inf 0.130 0.8966Longitudinal Analyses
Extraversion
extrav <- glm(value ~ race * sex * extra_tipi * school_group,
family = binomial, data = nlsy_l)
summary(extrav)##
## Call:
## glm(formula = value ~ race * sex * extra_tipi * school_group,
## family = binomial, data = nlsy_l)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9603 -0.4986 -0.3151 -0.2249 3.3803
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -3.47092 0.55419 -6.263
## racelatinx 1.42570 1.44192 0.989
## raceblack 1.82868 0.63909 2.861
## raceother 1.57890 0.88473 1.785
## sexFEMALE -0.77806 1.23157 -0.632
## extra_tipi -0.02769 0.11149 -0.248
## school_groupmiddle 1.64313 0.61615 2.667
## school_grouphigh 1.99167 0.67200 2.964
## racelatinx:sexFEMALE -11.74280 488.85864 -0.024
## raceblack:sexFEMALE -0.25586 1.41185 -0.181
## raceother:sexFEMALE -2.70980 2.35880 -1.149
## racelatinx:extra_tipi -0.04776 0.29599 -0.161
## raceblack:extra_tipi -0.06826 0.13027 -0.524
## raceother:extra_tipi -0.13980 0.18486 -0.756
## sexFEMALE:extra_tipi -0.18102 0.25454 -0.711
## racelatinx:school_groupmiddle -0.32227 1.64279 -0.196
## raceblack:school_groupmiddle -0.96814 0.71810 -1.348
## raceother:school_groupmiddle -1.33465 1.03639 -1.288
## racelatinx:school_grouphigh -3.67299 1.93703 -1.896
## raceblack:school_grouphigh -0.75841 0.79469 -0.954
## raceother:school_grouphigh -0.82085 1.17918 -0.696
## sexFEMALE:school_groupmiddle -0.33107 1.33107 -0.249
## sexFEMALE:school_grouphigh 0.17172 1.39078 0.123
## extra_tipi:school_groupmiddle -0.01555 0.12437 -0.125
## extra_tipi:school_grouphigh 0.02214 0.13524 0.164
## racelatinx:sexFEMALE:extra_tipi 0.25647 95.78405 0.003
## raceblack:sexFEMALE:extra_tipi 0.09219 0.29583 0.312
## raceother:sexFEMALE:extra_tipi 0.55467 0.45970 1.207
## racelatinx:sexFEMALE:school_groupmiddle 9.12578 488.86727 0.019
## raceblack:sexFEMALE:school_groupmiddle 0.43995 1.53574 0.286
## raceother:sexFEMALE:school_groupmiddle 2.69952 2.61461 1.032
## racelatinx:sexFEMALE:school_grouphigh 10.84382 488.86507 0.022
## raceblack:sexFEMALE:school_grouphigh -0.37139 1.61678 -0.230
## raceother:sexFEMALE:school_grouphigh 1.93106 2.76598 0.698
## racelatinx:extra_tipi:school_groupmiddle -0.18562 0.34734 -0.534
## raceblack:extra_tipi:school_groupmiddle 0.06466 0.14694 0.440
## raceother:extra_tipi:school_groupmiddle 0.15957 0.21563 0.740
## racelatinx:extra_tipi:school_grouphigh 0.42264 0.38587 1.095
## raceblack:extra_tipi:school_grouphigh -0.05199 0.16263 -0.320
## raceother:extra_tipi:school_grouphigh 0.03542 0.24534 0.144
## sexFEMALE:extra_tipi:school_groupmiddle 0.21064 0.27374 0.770
## sexFEMALE:extra_tipi:school_grouphigh 0.11981 0.28531 0.420
## racelatinx:sexFEMALE:extra_tipi:school_groupmiddle 0.13887 95.78579 0.001
## raceblack:sexFEMALE:extra_tipi:school_groupmiddle -0.12374 0.32014 -0.387
## raceother:sexFEMALE:extra_tipi:school_groupmiddle -0.67584 0.51485 -1.313
## racelatinx:sexFEMALE:extra_tipi:school_grouphigh -0.02773 95.78510 0.000
## raceblack:sexFEMALE:extra_tipi:school_grouphigh 0.07490 0.33637 0.223
## raceother:sexFEMALE:extra_tipi:school_grouphigh -0.40167 0.54158 -0.742
## Pr(>|z|)
## (Intercept) 3.78e-10 ***
## racelatinx 0.32278
## raceblack 0.00422 **
## raceother 0.07432 .
## sexFEMALE 0.52754
## extra_tipi 0.80387
## school_groupmiddle 0.00766 **
## school_grouphigh 0.00304 **
## racelatinx:sexFEMALE 0.98084
## raceblack:sexFEMALE 0.85619
## raceother:sexFEMALE 0.25064
## racelatinx:extra_tipi 0.87181
## raceblack:extra_tipi 0.60026
## raceother:extra_tipi 0.44949
## sexFEMALE:extra_tipi 0.47697
## racelatinx:school_groupmiddle 0.84448
## raceblack:school_groupmiddle 0.17760
## raceother:school_groupmiddle 0.19782
## racelatinx:school_grouphigh 0.05793 .
## raceblack:school_grouphigh 0.33991
## raceother:school_grouphigh 0.48636
## sexFEMALE:school_groupmiddle 0.80358
## sexFEMALE:school_grouphigh 0.90174
## extra_tipi:school_groupmiddle 0.90048
## extra_tipi:school_grouphigh 0.86994
## racelatinx:sexFEMALE:extra_tipi 0.99786
## raceblack:sexFEMALE:extra_tipi 0.75533
## raceother:sexFEMALE:extra_tipi 0.22759
## racelatinx:sexFEMALE:school_groupmiddle 0.98511
## raceblack:sexFEMALE:school_groupmiddle 0.77451
## raceother:sexFEMALE:school_groupmiddle 0.30185
## racelatinx:sexFEMALE:school_grouphigh 0.98230
## raceblack:sexFEMALE:school_grouphigh 0.81832
## raceother:sexFEMALE:school_grouphigh 0.48509
## racelatinx:extra_tipi:school_groupmiddle 0.59306
## raceblack:extra_tipi:school_groupmiddle 0.65989
## raceother:extra_tipi:school_groupmiddle 0.45929
## racelatinx:extra_tipi:school_grouphigh 0.27339
## raceblack:extra_tipi:school_grouphigh 0.74923
## raceother:extra_tipi:school_grouphigh 0.88522
## sexFEMALE:extra_tipi:school_groupmiddle 0.44159
## sexFEMALE:extra_tipi:school_grouphigh 0.67454
## racelatinx:sexFEMALE:extra_tipi:school_groupmiddle 0.99884
## raceblack:sexFEMALE:extra_tipi:school_groupmiddle 0.69912
## raceother:sexFEMALE:extra_tipi:school_groupmiddle 0.18929
## racelatinx:sexFEMALE:extra_tipi:school_grouphigh 0.99977
## raceblack:sexFEMALE:extra_tipi:school_grouphigh 0.82378
## raceother:sexFEMALE:extra_tipi:school_grouphigh 0.45830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11720 on 18336 degrees of freedom
## Residual deviance: 10431 on 18289 degrees of freedom
## AIC: 10527
##
## Number of Fisher Scoring iterations: 13plot_model(extrav, type = "pred", terms = c("extra_tipi", "race", "sex", "school_group"))
object1 <- emtrends(extrav, pairwise ~ race * sex * school_group, var = "extra_tipi", adjust = "none")
object1## $emtrends
## race sex school_group extra_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE elementary -0.02769 0.1115 Inf -0.2462 0.19082
## latinx MALE elementary -0.07545 0.2742 Inf -0.6129 0.46196
## black MALE elementary -0.09595 0.0674 Inf -0.2280 0.03612
## other MALE elementary -0.16749 0.1474 Inf -0.4565 0.12152
## white FEMALE elementary -0.20871 0.2288 Inf -0.6572 0.23978
## latinx FEMALE elementary 0.00000 95.7833 Inf -187.7319 187.73186
## black FEMALE elementary -0.18478 0.1349 Inf -0.4491 0.07953
## other FEMALE elementary 0.20616 0.3533 Inf -0.4862 0.89853
## white MALE middle -0.04324 0.0551 Inf -0.1513 0.06480
## latinx MALE middle -0.27662 0.1732 Inf -0.6161 0.06282
## black MALE middle -0.04684 0.0398 Inf -0.1248 0.03112
## other MALE middle -0.02347 0.0964 Inf -0.2124 0.16542
## white FEMALE middle -0.01362 0.0843 Inf -0.1788 0.15158
## latinx FEMALE middle 0.14834 0.5406 Inf -0.9112 1.20786
## black FEMALE middle -0.04877 0.0570 Inf -0.1605 0.06295
## other FEMALE middle -0.11502 0.1852 Inf -0.4781 0.24808
## white MALE high -0.00554 0.0766 Inf -0.1556 0.14450
## latinx MALE high 0.36934 0.2354 Inf -0.0921 0.83077
## black MALE high -0.12579 0.0602 Inf -0.2437 -0.00789
## other MALE high -0.10993 0.1420 Inf -0.3882 0.16836
## white FEMALE high -0.06676 0.1037 Inf -0.2700 0.13644
## latinx FEMALE high 0.53686 0.3591 Inf -0.1669 1.24063
## black FEMALE high -0.01992 0.0735 Inf -0.1640 0.12414
## other FEMALE high -0.01814 0.2127 Inf -0.4349 0.39865
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df
## white MALE elementary - latinx MALE elementary 0.04776 0.2960 Inf
## white MALE elementary - black MALE elementary 0.06826 0.1303 Inf
## white MALE elementary - other MALE elementary 0.13980 0.1849 Inf
## white MALE elementary - white FEMALE elementary 0.18102 0.2545 Inf
## white MALE elementary - latinx FEMALE elementary -0.02769 95.7834 Inf
## white MALE elementary - black FEMALE elementary 0.15710 0.1750 Inf
## white MALE elementary - other FEMALE elementary -0.23385 0.3704 Inf
## white MALE elementary - white MALE middle 0.01555 0.1244 Inf
## white MALE elementary - latinx MALE middle 0.24894 0.2060 Inf
## white MALE elementary - black MALE middle 0.01916 0.1184 Inf
## white MALE elementary - other MALE middle -0.00422 0.1474 Inf
## white MALE elementary - white FEMALE middle -0.01407 0.1398 Inf
## white MALE elementary - latinx FEMALE middle -0.17602 0.5520 Inf
## white MALE elementary - black FEMALE middle 0.02108 0.1252 Inf
## white MALE elementary - other FEMALE middle 0.08733 0.2162 Inf
## white MALE elementary - white MALE high -0.02214 0.1352 Inf
## white MALE elementary - latinx MALE high -0.39703 0.2605 Inf
## white MALE elementary - black MALE high 0.09811 0.1267 Inf
## white MALE elementary - other MALE high 0.08224 0.1805 Inf
## white MALE elementary - white FEMALE high 0.03907 0.1522 Inf
## white MALE elementary - latinx FEMALE high -0.56455 0.3760 Inf
## white MALE elementary - black FEMALE high -0.00777 0.1335 Inf
## white MALE elementary - other FEMALE high -0.00955 0.2401 Inf
## latinx MALE elementary - black MALE elementary 0.02050 0.2823 Inf
## latinx MALE elementary - other MALE elementary 0.09204 0.3113 Inf
## latinx MALE elementary - white FEMALE elementary 0.13326 0.3571 Inf
## latinx MALE elementary - latinx FEMALE elementary -0.07545 95.7837 Inf
## latinx MALE elementary - black FEMALE elementary 0.10934 0.3056 Inf
## latinx MALE elementary - other FEMALE elementary -0.28161 0.4472 Inf
## latinx MALE elementary - white MALE middle -0.03221 0.2797 Inf
## latinx MALE elementary - latinx MALE middle 0.20118 0.3243 Inf
## latinx MALE elementary - black MALE middle -0.02861 0.2771 Inf
## latinx MALE elementary - other MALE middle -0.05198 0.2906 Inf
## latinx MALE elementary - white FEMALE middle -0.06183 0.2868 Inf
## latinx MALE elementary - latinx FEMALE middle -0.22379 0.6061 Inf
## latinx MALE elementary - black FEMALE middle -0.02668 0.2801 Inf
## latinx MALE elementary - other FEMALE middle 0.03957 0.3309 Inf
## latinx MALE elementary - white MALE high -0.06991 0.2847 Inf
## latinx MALE elementary - latinx MALE high -0.44479 0.3614 Inf
## latinx MALE elementary - black MALE high 0.05034 0.2807 Inf
## latinx MALE elementary - other MALE high 0.03448 0.3088 Inf
## latinx MALE elementary - white FEMALE high -0.00869 0.2931 Inf
## latinx MALE elementary - latinx FEMALE high -0.61231 0.4518 Inf
## latinx MALE elementary - black FEMALE high -0.05553 0.2839 Inf
## latinx MALE elementary - other FEMALE high -0.05731 0.3470 Inf
## black MALE elementary - other MALE elementary 0.07154 0.1621 Inf
## black MALE elementary - white FEMALE elementary 0.11276 0.2385 Inf
## black MALE elementary - latinx FEMALE elementary -0.09595 95.7833 Inf
## black MALE elementary - black FEMALE elementary 0.08883 0.1508 Inf
## black MALE elementary - other FEMALE elementary -0.30211 0.3596 Inf
## black MALE elementary - white MALE middle -0.05271 0.0871 Inf
## black MALE elementary - latinx MALE middle 0.18067 0.1858 Inf
## black MALE elementary - black MALE middle -0.04911 0.0783 Inf
## black MALE elementary - other MALE middle -0.07248 0.1176 Inf
## black MALE elementary - white FEMALE middle -0.08233 0.1079 Inf
## black MALE elementary - latinx FEMALE middle -0.24429 0.5448 Inf
## black MALE elementary - black FEMALE middle -0.04718 0.0883 Inf
## black MALE elementary - other FEMALE middle 0.01906 0.1971 Inf
## black MALE elementary - white MALE high -0.09041 0.1020 Inf
## black MALE elementary - latinx MALE high -0.46529 0.2449 Inf
## black MALE elementary - black MALE high 0.02984 0.0903 Inf
## black MALE elementary - other MALE high 0.01398 0.1572 Inf
## black MALE elementary - white FEMALE high -0.02919 0.1236 Inf
## black MALE elementary - latinx FEMALE high -0.63281 0.3653 Inf
## black MALE elementary - black FEMALE high -0.07604 0.0997 Inf
## black MALE elementary - other FEMALE high -0.07781 0.2231 Inf
## other MALE elementary - white FEMALE elementary 0.04122 0.2722 Inf
## other MALE elementary - latinx FEMALE elementary -0.16749 95.7834 Inf
## other MALE elementary - black FEMALE elementary 0.01730 0.1998 Inf
## other MALE elementary - other FEMALE elementary -0.37365 0.3828 Inf
## other MALE elementary - white MALE middle -0.12425 0.1574 Inf
## other MALE elementary - latinx MALE middle 0.10914 0.2275 Inf
## other MALE elementary - black MALE middle -0.12065 0.1527 Inf
## other MALE elementary - other MALE middle -0.14402 0.1762 Inf
## other MALE elementary - white FEMALE middle -0.15387 0.1698 Inf
## other MALE elementary - latinx FEMALE middle -0.31582 0.5603 Inf
## other MALE elementary - black FEMALE middle -0.11872 0.1581 Inf
## other MALE elementary - other FEMALE middle -0.05247 0.2368 Inf
## other MALE elementary - white MALE high -0.16194 0.1661 Inf
## other MALE elementary - latinx MALE high -0.53683 0.2778 Inf
## other MALE elementary - black MALE high -0.04169 0.1593 Inf
## other MALE elementary - other MALE high -0.05756 0.2047 Inf
## other MALE elementary - white FEMALE high -0.10073 0.1802 Inf
## other MALE elementary - latinx FEMALE high -0.70435 0.3882 Inf
## other MALE elementary - black FEMALE high -0.14757 0.1648 Inf
## other MALE elementary - other FEMALE high -0.14935 0.2588 Inf
## white FEMALE elementary - latinx FEMALE elementary -0.20871 95.7836 Inf
## white FEMALE elementary - black FEMALE elementary -0.02392 0.2656 Inf
## white FEMALE elementary - other FEMALE elementary -0.41487 0.4209 Inf
## white FEMALE elementary - white MALE middle -0.16547 0.2354 Inf
## white FEMALE elementary - latinx MALE middle 0.06792 0.2870 Inf
## white FEMALE elementary - black MALE middle -0.16187 0.2323 Inf
## white FEMALE elementary - other MALE middle -0.18524 0.2483 Inf
## white FEMALE elementary - white FEMALE middle -0.19509 0.2439 Inf
## white FEMALE elementary - latinx FEMALE middle -0.35705 0.5870 Inf
## white FEMALE elementary - black FEMALE middle -0.15994 0.2358 Inf
## white FEMALE elementary - other FEMALE middle -0.09369 0.2944 Inf
## white FEMALE elementary - white MALE high -0.20317 0.2413 Inf
## white FEMALE elementary - latinx MALE high -0.57805 0.3283 Inf
## white FEMALE elementary - black MALE high -0.08292 0.2366 Inf
## white FEMALE elementary - other MALE high -0.09878 0.2693 Inf
## white FEMALE elementary - white FEMALE high -0.14195 0.2512 Inf
## white FEMALE elementary - latinx FEMALE high -0.74557 0.4258 Inf
## white FEMALE elementary - black FEMALE high -0.18879 0.2403 Inf
## white FEMALE elementary - other FEMALE high -0.19057 0.3124 Inf
## latinx FEMALE elementary - black FEMALE elementary 0.18478 95.7834 Inf
## latinx FEMALE elementary - other FEMALE elementary -0.20616 95.7840 Inf
## latinx FEMALE elementary - white MALE middle 0.04324 95.7833 Inf
## latinx FEMALE elementary - latinx MALE middle 0.27662 95.7835 Inf
## latinx FEMALE elementary - black MALE middle 0.04684 95.7833 Inf
## latinx FEMALE elementary - other MALE middle 0.02347 95.7834 Inf
## latinx FEMALE elementary - white FEMALE middle 0.01362 95.7834 Inf
## latinx FEMALE elementary - latinx FEMALE middle -0.14834 95.7849 Inf
## latinx FEMALE elementary - black FEMALE middle 0.04877 95.7833 Inf
## latinx FEMALE elementary - other FEMALE middle 0.11502 95.7835 Inf
## latinx FEMALE elementary - white MALE high 0.00554 95.7833 Inf
## latinx FEMALE elementary - latinx MALE high -0.36934 95.7836 Inf
## latinx FEMALE elementary - black MALE high 0.12579 95.7833 Inf
## latinx FEMALE elementary - other MALE high 0.10993 95.7834 Inf
## latinx FEMALE elementary - white FEMALE high 0.06676 95.7834 Inf
## latinx FEMALE elementary - latinx FEMALE high -0.53686 95.7840 Inf
## latinx FEMALE elementary - black FEMALE high 0.01992 95.7833 Inf
## latinx FEMALE elementary - other FEMALE high 0.01814 95.7836 Inf
## black FEMALE elementary - other FEMALE elementary -0.39095 0.3781 Inf
## black FEMALE elementary - white MALE middle -0.14154 0.1457 Inf
## black FEMALE elementary - latinx MALE middle 0.09184 0.2195 Inf
## black FEMALE elementary - black MALE middle -0.13794 0.1406 Inf
## black FEMALE elementary - other MALE middle -0.16132 0.1658 Inf
## black FEMALE elementary - white FEMALE middle -0.17117 0.1590 Inf
## black FEMALE elementary - latinx FEMALE middle -0.33312 0.5572 Inf
## black FEMALE elementary - black FEMALE middle -0.13602 0.1464 Inf
## black FEMALE elementary - other FEMALE middle -0.06977 0.2291 Inf
## black FEMALE elementary - white MALE high -0.17924 0.1551 Inf
## black FEMALE elementary - latinx MALE high -0.55412 0.2713 Inf
## black FEMALE elementary - black MALE high -0.05899 0.1477 Inf
## black FEMALE elementary - other MALE high -0.07486 0.1958 Inf
## black FEMALE elementary - white FEMALE high -0.11803 0.1701 Inf
## black FEMALE elementary - latinx FEMALE high -0.72165 0.3836 Inf
## black FEMALE elementary - black FEMALE high -0.16487 0.1536 Inf
## black FEMALE elementary - other FEMALE high -0.16665 0.2518 Inf
## other FEMALE elementary - white MALE middle 0.24940 0.3575 Inf
## other FEMALE elementary - latinx MALE middle 0.48279 0.3934 Inf
## other FEMALE elementary - black MALE middle 0.25300 0.3555 Inf
## other FEMALE elementary - other MALE middle 0.22963 0.3662 Inf
## other FEMALE elementary - white FEMALE middle 0.21978 0.3632 Inf
## other FEMALE elementary - latinx FEMALE middle 0.05783 0.6458 Inf
## other FEMALE elementary - black FEMALE middle 0.25493 0.3578 Inf
## other FEMALE elementary - other FEMALE middle 0.32118 0.3989 Inf
## other FEMALE elementary - white MALE high 0.21171 0.3615 Inf
## other FEMALE elementary - latinx MALE high -0.16318 0.4245 Inf
## other FEMALE elementary - black MALE high 0.33196 0.3583 Inf
## other FEMALE elementary - other MALE high 0.31609 0.3807 Inf
## other FEMALE elementary - white FEMALE high 0.27292 0.3682 Inf
## other FEMALE elementary - latinx FEMALE high -0.33070 0.5037 Inf
## other FEMALE elementary - black FEMALE high 0.22608 0.3608 Inf
## other FEMALE elementary - other FEMALE high 0.22430 0.4123 Inf
## white MALE middle - latinx MALE middle 0.23338 0.1817 Inf
## white MALE middle - black MALE middle 0.00360 0.0680 Inf
## white MALE middle - other MALE middle -0.01977 0.1110 Inf
## white MALE middle - white FEMALE middle -0.02962 0.1007 Inf
## white MALE middle - latinx FEMALE middle -0.19158 0.5434 Inf
## white MALE middle - black FEMALE middle 0.00553 0.0793 Inf
## white MALE middle - other FEMALE middle 0.07178 0.1933 Inf
## white MALE middle - white MALE high -0.03770 0.0943 Inf
## white MALE middle - latinx MALE high -0.41258 0.2418 Inf
## white MALE middle - black MALE high 0.08255 0.0816 Inf
## white MALE middle - other MALE high 0.06669 0.1523 Inf
## white MALE middle - white FEMALE high 0.02352 0.1174 Inf
## white MALE middle - latinx FEMALE high -0.58010 0.3633 Inf
## white MALE middle - black FEMALE high -0.02332 0.0919 Inf
## white MALE middle - other FEMALE high -0.02510 0.2197 Inf
## latinx MALE middle - black MALE middle -0.22978 0.1777 Inf
## latinx MALE middle - other MALE middle -0.25316 0.1982 Inf
## latinx MALE middle - white FEMALE middle -0.26301 0.1926 Inf
## latinx MALE middle - latinx FEMALE middle -0.42496 0.5676 Inf
## latinx MALE middle - black FEMALE middle -0.22786 0.1823 Inf
## latinx MALE middle - other FEMALE middle -0.16161 0.2536 Inf
## latinx MALE middle - white MALE high -0.27108 0.1893 Inf
## latinx MALE middle - latinx MALE high -0.64596 0.2923 Inf
## latinx MALE middle - black MALE high -0.15083 0.1833 Inf
## latinx MALE middle - other MALE high -0.16670 0.2240 Inf
## latinx MALE middle - white FEMALE high -0.20987 0.2019 Inf
## latinx MALE middle - latinx FEMALE high -0.81349 0.3987 Inf
## latinx MALE middle - black FEMALE high -0.25671 0.1881 Inf
## latinx MALE middle - other FEMALE high -0.25849 0.2742 Inf
## black MALE middle - other MALE middle -0.02337 0.1043 Inf
## black MALE middle - white FEMALE middle -0.03322 0.0932 Inf
## black MALE middle - latinx FEMALE middle -0.19518 0.5421 Inf
## black MALE middle - black FEMALE middle 0.00193 0.0695 Inf
## black MALE middle - other FEMALE middle 0.06817 0.1895 Inf
## black MALE middle - white MALE high -0.04130 0.0863 Inf
## black MALE middle - latinx MALE high -0.41618 0.2388 Inf
## black MALE middle - black MALE high 0.07895 0.0721 Inf
## black MALE middle - other MALE high 0.06308 0.1474 Inf
## black MALE middle - white FEMALE high 0.01992 0.1110 Inf
## black MALE middle - latinx FEMALE high -0.58371 0.3613 Inf
## black MALE middle - black FEMALE high -0.02693 0.0836 Inf
## black MALE middle - other FEMALE high -0.02871 0.2163 Inf
## other MALE middle - white FEMALE middle -0.00985 0.1280 Inf
## other MALE middle - latinx FEMALE middle -0.17181 0.5491 Inf
## other MALE middle - black FEMALE middle 0.02530 0.1120 Inf
## other MALE middle - other FEMALE middle 0.09155 0.2088 Inf
## other MALE middle - white MALE high -0.01793 0.1231 Inf
## other MALE middle - latinx MALE high -0.39281 0.2544 Inf
## other MALE middle - black MALE high 0.10232 0.1136 Inf
## other MALE middle - other MALE high 0.08646 0.1716 Inf
## other MALE middle - white FEMALE high 0.04329 0.1416 Inf
## other MALE middle - latinx FEMALE high -0.56033 0.3718 Inf
## other MALE middle - black FEMALE high -0.00355 0.1212 Inf
## other MALE middle - other FEMALE high -0.00533 0.2335 Inf
## white FEMALE middle - latinx FEMALE middle -0.16196 0.5471 Inf
## white FEMALE middle - black FEMALE middle 0.03515 0.1017 Inf
## white FEMALE middle - other FEMALE middle 0.10140 0.2035 Inf
## white FEMALE middle - white MALE high -0.00808 0.1139 Inf
## white FEMALE middle - latinx MALE high -0.38296 0.2501 Inf
## white FEMALE middle - black MALE high 0.11217 0.1036 Inf
## white FEMALE middle - other MALE high 0.09631 0.1651 Inf
## white FEMALE middle - white FEMALE high 0.05314 0.1336 Inf
## white FEMALE middle - latinx FEMALE high -0.55048 0.3688 Inf
## white FEMALE middle - black FEMALE high 0.00630 0.1118 Inf
## white FEMALE middle - other FEMALE high 0.00452 0.2288 Inf
## latinx FEMALE middle - black FEMALE middle 0.19711 0.5436 Inf
## latinx FEMALE middle - other FEMALE middle 0.26335 0.5715 Inf
## latinx FEMALE middle - white MALE high 0.15388 0.5460 Inf
## latinx FEMALE middle - latinx MALE high -0.22100 0.5896 Inf
## latinx FEMALE middle - black MALE high 0.27413 0.5439 Inf
## latinx FEMALE middle - other MALE high 0.25826 0.5589 Inf
## latinx FEMALE middle - white FEMALE high 0.21509 0.5504 Inf
## latinx FEMALE middle - latinx FEMALE high -0.38853 0.6490 Inf
## latinx FEMALE middle - black FEMALE high 0.16825 0.5456 Inf
## latinx FEMALE middle - other FEMALE high 0.16647 0.5809 Inf
## black FEMALE middle - other FEMALE middle 0.06625 0.1938 Inf
## black FEMALE middle - white MALE high -0.04323 0.0954 Inf
## black FEMALE middle - latinx MALE high -0.41811 0.2422 Inf
## black FEMALE middle - black MALE high 0.07702 0.0829 Inf
## black FEMALE middle - other MALE high 0.06116 0.1530 Inf
## black FEMALE middle - white FEMALE high 0.01799 0.1183 Inf
## black FEMALE middle - latinx FEMALE high -0.58563 0.3636 Inf
## black FEMALE middle - black FEMALE high -0.02885 0.0930 Inf
## black FEMALE middle - other FEMALE high -0.03063 0.2202 Inf
## other FEMALE middle - white MALE high -0.10947 0.2004 Inf
## other FEMALE middle - latinx MALE high -0.48435 0.2996 Inf
## other FEMALE middle - black MALE high 0.01078 0.1948 Inf
## other FEMALE middle - other MALE high -0.00509 0.2334 Inf
## other FEMALE middle - white FEMALE high -0.04826 0.2123 Inf
## other FEMALE middle - latinx FEMALE high -0.65188 0.4040 Inf
## other FEMALE middle - black FEMALE high -0.09510 0.1993 Inf
## other FEMALE middle - other FEMALE high -0.09688 0.2820 Inf
## white MALE high - latinx MALE high -0.37488 0.2476 Inf
## white MALE high - black MALE high 0.12025 0.0974 Inf
## white MALE high - other MALE high 0.10438 0.1613 Inf
## white MALE high - white FEMALE high 0.06121 0.1289 Inf
## white MALE high - latinx FEMALE high -0.54241 0.3671 Inf
## white MALE high - black FEMALE high 0.01437 0.1061 Inf
## white MALE high - other FEMALE high 0.01259 0.2260 Inf
## latinx MALE high - black MALE high 0.49513 0.2430 Inf
## latinx MALE high - other MALE high 0.47927 0.2749 Inf
## latinx MALE high - white FEMALE high 0.43610 0.2572 Inf
## latinx MALE high - latinx FEMALE high -0.16752 0.4294 Inf
## latinx MALE high - black FEMALE high 0.38925 0.2466 Inf
## latinx MALE high - other FEMALE high 0.38748 0.3172 Inf
## black MALE high - other MALE high -0.01587 0.1542 Inf
## black MALE high - white FEMALE high -0.05904 0.1199 Inf
## black MALE high - latinx FEMALE high -0.66266 0.3641 Inf
## black MALE high - black FEMALE high -0.10588 0.0950 Inf
## black MALE high - other FEMALE high -0.10766 0.2210 Inf
## other MALE high - white FEMALE high -0.04317 0.1758 Inf
## other MALE high - latinx FEMALE high -0.64679 0.3861 Inf
## other MALE high - black FEMALE high -0.09001 0.1599 Inf
## other MALE high - other FEMALE high -0.09179 0.2557 Inf
## white FEMALE high - latinx FEMALE high -0.60362 0.3737 Inf
## white FEMALE high - black FEMALE high -0.04684 0.1271 Inf
## white FEMALE high - other FEMALE high -0.04862 0.2366 Inf
## latinx FEMALE high - black FEMALE high 0.55678 0.3665 Inf
## latinx FEMALE high - other FEMALE high 0.55500 0.4173 Inf
## black FEMALE high - other FEMALE high -0.00178 0.2250 Inf
## z.ratio p.value
## 0.161 0.8718
## 0.524 0.6003
## 0.756 0.4495
## 0.711 0.4770
## 0.000 0.9998
## 0.898 0.3693
## -0.631 0.5279
## 0.125 0.9005
## 1.209 0.2268
## 0.162 0.8714
## -0.029 0.9772
## -0.101 0.9198
## -0.319 0.7498
## 0.168 0.8663
## 0.404 0.6863
## -0.164 0.8699
## -1.524 0.1275
## 0.774 0.4387
## 0.456 0.6487
## 0.257 0.7975
## -1.502 0.1332
## -0.058 0.9536
## -0.040 0.9683
## 0.073 0.9421
## 0.296 0.7675
## 0.373 0.7090
## -0.001 0.9994
## 0.358 0.7205
## -0.630 0.5289
## -0.115 0.9083
## 0.620 0.5350
## -0.103 0.9178
## -0.179 0.8581
## -0.216 0.8293
## -0.369 0.7120
## -0.095 0.9241
## 0.120 0.9048
## -0.246 0.8060
## -1.231 0.2184
## 0.179 0.8577
## 0.112 0.9111
## -0.030 0.9763
## -1.355 0.1753
## -0.196 0.8449
## -0.165 0.8688
## 0.441 0.6590
## 0.473 0.6364
## -0.001 0.9992
## 0.589 0.5557
## -0.840 0.4009
## -0.605 0.5449
## 0.972 0.3309
## -0.628 0.5303
## -0.616 0.5376
## -0.763 0.4455
## -0.448 0.6538
## -0.535 0.5929
## 0.097 0.9230
## -0.886 0.3754
## -1.900 0.0574
## 0.330 0.7411
## 0.089 0.9291
## -0.236 0.8134
## -1.732 0.0833
## -0.763 0.4457
## -0.349 0.7272
## 0.151 0.8796
## -0.002 0.9986
## 0.087 0.9310
## -0.976 0.3290
## -0.789 0.4300
## 0.480 0.6314
## -0.790 0.4296
## -0.818 0.4136
## -0.906 0.3650
## -0.564 0.5730
## -0.751 0.4527
## -0.222 0.8246
## -0.975 0.3297
## -1.932 0.0533
## -0.262 0.7935
## -0.281 0.7786
## -0.559 0.5763
## -1.815 0.0696
## -0.896 0.3704
## -0.577 0.5638
## -0.002 0.9983
## -0.090 0.9282
## -0.986 0.3243
## -0.703 0.4820
## 0.237 0.8129
## -0.697 0.4858
## -0.746 0.4556
## -0.800 0.4237
## -0.608 0.5430
## -0.678 0.4976
## -0.318 0.7503
## -0.842 0.3998
## -1.761 0.0783
## -0.350 0.7260
## -0.367 0.7138
## -0.565 0.5720
## -1.751 0.0799
## -0.786 0.4321
## -0.610 0.5418
## 0.002 0.9985
## -0.002 0.9983
## 0.000 0.9996
## 0.003 0.9977
## 0.000 0.9996
## 0.000 0.9998
## 0.000 0.9999
## -0.002 0.9988
## 0.001 0.9996
## 0.001 0.9990
## 0.000 1.0000
## -0.004 0.9969
## 0.001 0.9990
## 0.001 0.9991
## 0.001 0.9994
## -0.006 0.9955
## 0.000 0.9998
## 0.000 0.9998
## -1.034 0.3012
## -0.972 0.3313
## 0.418 0.6757
## -0.981 0.3265
## -0.973 0.3304
## -1.076 0.2818
## -0.598 0.5499
## -0.929 0.3529
## -0.304 0.7608
## -1.156 0.2477
## -2.042 0.0411
## -0.399 0.6895
## -0.382 0.7023
## -0.694 0.4878
## -1.881 0.0599
## -1.073 0.2831
## -0.662 0.5081
## 0.698 0.4854
## 1.227 0.2198
## 0.712 0.4766
## 0.627 0.5306
## 0.605 0.5451
## 0.090 0.9286
## 0.712 0.4762
## 0.805 0.4207
## 0.586 0.5581
## -0.384 0.7007
## 0.926 0.3543
## 0.830 0.4064
## 0.741 0.4585
## -0.657 0.5115
## 0.627 0.5309
## 0.544 0.5864
## 1.284 0.1991
## 0.053 0.9577
## -0.178 0.8587
## -0.294 0.7687
## -0.353 0.7244
## 0.070 0.9444
## 0.371 0.7104
## -0.400 0.6894
## -1.706 0.0879
## 1.012 0.3116
## 0.438 0.6615
## 0.200 0.8413
## -1.597 0.1103
## -0.254 0.7996
## -0.114 0.9090
## -1.293 0.1960
## -1.277 0.2015
## -1.365 0.1721
## -0.749 0.4541
## -1.250 0.2114
## -0.637 0.5240
## -1.432 0.1523
## -2.210 0.0271
## -0.823 0.4107
## -0.744 0.4567
## -1.040 0.2985
## -2.041 0.0413
## -1.364 0.1724
## -0.943 0.3459
## -0.224 0.8226
## -0.356 0.7215
## -0.360 0.7188
## 0.028 0.9779
## 0.360 0.7190
## -0.479 0.6321
## -1.743 0.0813
## 1.095 0.2736
## 0.428 0.6688
## 0.179 0.8577
## -1.616 0.1062
## -0.322 0.7473
## -0.133 0.8944
## -0.077 0.9387
## -0.313 0.7544
## 0.226 0.8212
## 0.438 0.6611
## -0.146 0.8842
## -1.544 0.1226
## 0.901 0.3677
## 0.504 0.6144
## 0.306 0.7597
## -1.507 0.1318
## -0.029 0.9766
## -0.023 0.9818
## -0.296 0.7672
## 0.345 0.7297
## 0.498 0.6183
## -0.071 0.9435
## -1.531 0.1257
## 1.083 0.2787
## 0.583 0.5597
## 0.398 0.6908
## -1.493 0.1356
## 0.056 0.9551
## 0.020 0.9842
## 0.363 0.7169
## 0.461 0.6449
## 0.282 0.7781
## -0.375 0.7078
## 0.504 0.6143
## 0.462 0.6440
## 0.391 0.6960
## -0.599 0.5494
## 0.308 0.7578
## 0.287 0.7744
## 0.342 0.7325
## -0.453 0.6506
## -1.726 0.0843
## 0.929 0.3527
## 0.400 0.6894
## 0.152 0.8792
## -1.611 0.1072
## -0.310 0.7564
## -0.139 0.8893
## -0.546 0.5850
## -1.617 0.1059
## 0.055 0.9559
## -0.022 0.9826
## -0.227 0.8202
## -1.613 0.1067
## -0.477 0.6332
## -0.344 0.7312
## -1.514 0.1300
## 1.235 0.2168
## 0.647 0.5176
## 0.475 0.6348
## -1.477 0.1396
## 0.135 0.8923
## 0.056 0.9556
## 2.038 0.0416
## 1.743 0.0813
## 1.695 0.0900
## -0.390 0.6964
## 1.578 0.1145
## 1.221 0.2220
## -0.103 0.9180
## -0.493 0.6223
## -1.820 0.0687
## -1.115 0.2649
## -0.487 0.6262
## -0.246 0.8060
## -1.675 0.0939
## -0.563 0.5734
## -0.359 0.7196
## -1.615 0.1063
## -0.369 0.7124
## -0.206 0.8372
## 1.519 0.1287
## 1.330 0.1835
## -0.008 0.9937Agreeableness
agree <- glm(ever_suspended ~ race * sex * agree_tipi * school_group,
family = binomial, data = nlsy_l)
summary(agree)##
## Call:
## glm(formula = ever_suspended ~ race * sex * agree_tipi * school_group,
## family = binomial, data = nlsy_l)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1831 -0.6956 -0.5332 -0.3484 2.6328
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.957631 0.270992 -3.534
## racelatinx 0.049003 1.176753 0.042
## raceblack 0.996077 0.359810 2.768
## raceother -0.028503 0.536854 -0.053
## sexFEMALE -1.100523 0.523296 -2.103
## agree_tipi -0.167547 0.056802 -2.950
## school_groupmiddle -0.015760 0.367799 -0.043
## school_grouphigh -0.002253 0.467376 -0.005
## racelatinx:sexFEMALE -1.756977 3.305054 -0.532
## raceblack:sexFEMALE 0.349160 0.657939 0.531
## raceother:sexFEMALE 0.862335 1.021766 0.844
## racelatinx:agree_tipi 0.115798 0.224787 0.515
## raceblack:agree_tipi 0.111908 0.075024 1.492
## raceother:agree_tipi 0.153016 0.109979 1.391
## sexFEMALE:agree_tipi 0.037851 0.104429 0.362
## racelatinx:school_groupmiddle 0.079730 1.471822 0.054
## raceblack:school_groupmiddle -0.174107 0.478948 -0.364
## raceother:school_groupmiddle 0.455882 0.738654 0.617
## racelatinx:school_grouphigh 0.200366 1.673116 0.120
## raceblack:school_grouphigh 0.150312 0.608464 0.247
## raceother:school_grouphigh -0.803652 1.012449 -0.794
## sexFEMALE:school_groupmiddle 0.222509 0.693222 0.321
## sexFEMALE:school_grouphigh 0.537235 0.854552 0.629
## agree_tipi:school_groupmiddle 0.060137 0.076938 0.782
## agree_tipi:school_grouphigh 0.117509 0.096762 1.214
## racelatinx:sexFEMALE:agree_tipi 0.087682 0.641681 0.137
## raceblack:sexFEMALE:agree_tipi -0.119006 0.131619 -0.904
## raceother:sexFEMALE:agree_tipi -0.261818 0.208050 -1.258
## racelatinx:sexFEMALE:school_groupmiddle -2.700278 4.076374 -0.662
## raceblack:sexFEMALE:school_groupmiddle -0.108144 0.864456 -0.125
## raceother:sexFEMALE:school_groupmiddle -1.638099 1.441124 -1.137
## racelatinx:sexFEMALE:school_grouphigh 2.480890 3.735697 0.664
## raceblack:sexFEMALE:school_grouphigh -1.354111 1.071433 -1.264
## raceother:sexFEMALE:school_grouphigh -0.284636 1.800720 -0.158
## racelatinx:agree_tipi:school_groupmiddle -0.072320 0.281374 -0.257
## raceblack:agree_tipi:school_groupmiddle -0.090445 0.099629 -0.908
## raceother:agree_tipi:school_groupmiddle -0.135586 0.150738 -0.899
## racelatinx:agree_tipi:school_grouphigh -0.087559 0.318341 -0.275
## raceblack:agree_tipi:school_grouphigh -0.177200 0.125323 -1.414
## raceother:agree_tipi:school_grouphigh 0.089580 0.202704 0.442
## sexFEMALE:agree_tipi:school_groupmiddle -0.012176 0.138062 -0.088
## sexFEMALE:agree_tipi:school_grouphigh -0.089845 0.169407 -0.530
## racelatinx:sexFEMALE:agree_tipi:school_groupmiddle 0.595968 0.766903 0.777
## raceblack:sexFEMALE:agree_tipi:school_groupmiddle 0.042404 0.172466 0.246
## raceother:sexFEMALE:agree_tipi:school_groupmiddle 0.297845 0.287024 1.038
## racelatinx:sexFEMALE:agree_tipi:school_grouphigh -0.205147 0.725196 -0.283
## raceblack:sexFEMALE:agree_tipi:school_grouphigh 0.328334 0.211530 1.552
## raceother:sexFEMALE:agree_tipi:school_grouphigh 0.090917 0.356356 0.255
## Pr(>|z|)
## (Intercept) 0.00041 ***
## racelatinx 0.96678
## raceblack 0.00563 **
## raceother 0.95766
## sexFEMALE 0.03546 *
## agree_tipi 0.00318 **
## school_groupmiddle 0.96582
## school_grouphigh 0.99615
## racelatinx:sexFEMALE 0.59500
## raceblack:sexFEMALE 0.59564
## raceother:sexFEMALE 0.39869
## racelatinx:agree_tipi 0.60645
## raceblack:agree_tipi 0.13580
## raceother:agree_tipi 0.16413
## sexFEMALE:agree_tipi 0.71701
## racelatinx:school_groupmiddle 0.95680
## raceblack:school_groupmiddle 0.71622
## raceother:school_groupmiddle 0.53712
## racelatinx:school_grouphigh 0.90468
## raceblack:school_grouphigh 0.80488
## raceother:school_grouphigh 0.42733
## sexFEMALE:school_groupmiddle 0.74823
## sexFEMALE:school_grouphigh 0.52956
## agree_tipi:school_groupmiddle 0.43443
## agree_tipi:school_grouphigh 0.22459
## racelatinx:sexFEMALE:agree_tipi 0.89131
## raceblack:sexFEMALE:agree_tipi 0.36590
## raceother:sexFEMALE:agree_tipi 0.20823
## racelatinx:sexFEMALE:school_groupmiddle 0.50770
## raceblack:sexFEMALE:school_groupmiddle 0.90044
## raceother:sexFEMALE:school_groupmiddle 0.25567
## racelatinx:sexFEMALE:school_grouphigh 0.50662
## raceblack:sexFEMALE:school_grouphigh 0.20629
## raceother:sexFEMALE:school_grouphigh 0.87440
## racelatinx:agree_tipi:school_groupmiddle 0.79716
## raceblack:agree_tipi:school_groupmiddle 0.36397
## raceother:agree_tipi:school_groupmiddle 0.36840
## racelatinx:agree_tipi:school_grouphigh 0.78328
## raceblack:agree_tipi:school_grouphigh 0.15738
## raceother:agree_tipi:school_grouphigh 0.65854
## sexFEMALE:agree_tipi:school_groupmiddle 0.92973
## sexFEMALE:agree_tipi:school_grouphigh 0.59587
## racelatinx:sexFEMALE:agree_tipi:school_groupmiddle 0.43709
## raceblack:sexFEMALE:agree_tipi:school_groupmiddle 0.80578
## raceother:sexFEMALE:agree_tipi:school_groupmiddle 0.29941
## racelatinx:sexFEMALE:agree_tipi:school_grouphigh 0.77726
## raceblack:sexFEMALE:agree_tipi:school_grouphigh 0.12062
## raceother:sexFEMALE:agree_tipi:school_grouphigh 0.79862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 18409 on 18336 degrees of freedom
## Residual deviance: 16950 on 18289 degrees of freedom
## AIC: 17046
##
## Number of Fisher Scoring iterations: 5plot_model(agree, type = "pred", terms = c("agree_tipi", "race", "sex", "school_group"))
object1 <- emtrends(agree, pairwise ~ race * sex * school_group, var = "agree_tipi", adjust = "none")
object1$contrasts## contrast estimate SE df
## white MALE elementary - latinx MALE elementary -1.16e-01 0.2248 Inf
## white MALE elementary - black MALE elementary -1.12e-01 0.0750 Inf
## white MALE elementary - other MALE elementary -1.53e-01 0.1100 Inf
## white MALE elementary - white FEMALE elementary -3.79e-02 0.1044 Inf
## white MALE elementary - latinx FEMALE elementary -2.41e-01 0.5973 Inf
## white MALE elementary - black FEMALE elementary -3.08e-02 0.0851 Inf
## white MALE elementary - other FEMALE elementary 7.10e-02 0.1635 Inf
## white MALE elementary - white MALE middle -6.01e-02 0.0769 Inf
## white MALE elementary - latinx MALE middle -1.04e-01 0.1708 Inf
## white MALE elementary - black MALE middle -8.16e-02 0.0695 Inf
## white MALE elementary - other MALE middle -7.76e-02 0.1056 Inf
## white MALE elementary - white FEMALE middle -8.58e-02 0.0932 Inf
## white MALE elementary - latinx FEMALE middle -8.13e-01 0.3815 Inf
## white MALE elementary - black FEMALE middle -3.07e-02 0.0767 Inf
## white MALE elementary - other FEMALE middle -1.39e-01 0.1620 Inf
## white MALE elementary - white MALE high -1.18e-01 0.0968 Inf
## white MALE elementary - latinx MALE high -1.46e-01 0.2189 Inf
## white MALE elementary - black MALE high -5.22e-02 0.0847 Inf
## white MALE elementary - other MALE high -3.60e-01 0.1615 Inf
## white MALE elementary - white FEMALE high -6.55e-02 0.1220 Inf
## white MALE elementary - latinx FEMALE high 2.37e-02 0.2343 Inf
## white MALE elementary - black FEMALE high -2.10e-01 0.0944 Inf
## white MALE elementary - other FEMALE high -1.37e-01 0.2151 Inf
## latinx MALE elementary - black MALE elementary 3.89e-03 0.2229 Inf
## latinx MALE elementary - other MALE elementary -3.72e-02 0.2370 Inf
## latinx MALE elementary - white FEMALE elementary 7.79e-02 0.2345 Inf
## latinx MALE elementary - latinx FEMALE elementary -1.26e-01 0.6331 Inf
## latinx MALE elementary - black FEMALE elementary 8.50e-02 0.2265 Inf
## latinx MALE elementary - other FEMALE elementary 1.87e-01 0.2661 Inf
## latinx MALE elementary - white MALE middle 5.57e-02 0.2236 Inf
## latinx MALE elementary - latinx MALE middle 1.22e-02 0.2707 Inf
## latinx MALE elementary - black MALE middle 3.42e-02 0.2211 Inf
## latinx MALE elementary - other MALE middle 3.82e-02 0.2350 Inf
## latinx MALE elementary - white FEMALE middle 3.00e-02 0.2297 Inf
## latinx MALE elementary - latinx FEMALE middle -6.97e-01 0.4354 Inf
## latinx MALE elementary - black FEMALE middle 8.51e-02 0.2235 Inf
## latinx MALE elementary - other FEMALE middle -2.35e-02 0.2652 Inf
## latinx MALE elementary - white MALE high -1.71e-03 0.2312 Inf
## latinx MALE elementary - latinx MALE high -2.99e-02 0.3033 Inf
## latinx MALE elementary - black MALE high 6.36e-02 0.2264 Inf
## latinx MALE elementary - other MALE high -2.44e-01 0.2649 Inf
## latinx MALE elementary - white FEMALE high 5.03e-02 0.2428 Inf
## latinx MALE elementary - latinx FEMALE high 1.40e-01 0.3146 Inf
## latinx MALE elementary - black FEMALE high -9.38e-02 0.2302 Inf
## latinx MALE elementary - other FEMALE high -2.14e-02 0.3006 Inf
## black MALE elementary - other MALE elementary -4.11e-02 0.1062 Inf
## black MALE elementary - white FEMALE elementary 7.41e-02 0.1004 Inf
## black MALE elementary - latinx FEMALE elementary -1.29e-01 0.5966 Inf
## black MALE elementary - black FEMALE elementary 8.12e-02 0.0801 Inf
## black MALE elementary - other FEMALE elementary 1.83e-01 0.1610 Inf
## black MALE elementary - white MALE middle 5.18e-02 0.0714 Inf
## black MALE elementary - latinx MALE middle 8.29e-03 0.1684 Inf
## black MALE elementary - black MALE middle 3.03e-02 0.0633 Inf
## black MALE elementary - other MALE middle 3.43e-02 0.1017 Inf
## black MALE elementary - white FEMALE middle 2.61e-02 0.0887 Inf
## black MALE elementary - latinx FEMALE middle -7.01e-01 0.3804 Inf
## black MALE elementary - black FEMALE middle 8.12e-02 0.0712 Inf
## black MALE elementary - other FEMALE middle -2.74e-02 0.1594 Inf
## black MALE elementary - white MALE high -5.60e-03 0.0924 Inf
## black MALE elementary - latinx MALE high -3.38e-02 0.2170 Inf
## black MALE elementary - black MALE high 5.97e-02 0.0796 Inf
## black MALE elementary - other MALE high -2.48e-01 0.1589 Inf
## black MALE elementary - white FEMALE high 4.64e-02 0.1186 Inf
## black MALE elementary - latinx FEMALE high 1.36e-01 0.2326 Inf
## black MALE elementary - black FEMALE high -9.76e-02 0.0899 Inf
## black MALE elementary - other FEMALE high -2.53e-02 0.2132 Inf
## other MALE elementary - white FEMALE elementary 1.15e-01 0.1286 Inf
## other MALE elementary - latinx FEMALE elementary -8.83e-02 0.6020 Inf
## other MALE elementary - black FEMALE elementary 1.22e-01 0.1135 Inf
## other MALE elementary - other FEMALE elementary 2.24e-01 0.1799 Inf
## other MALE elementary - white MALE middle 9.29e-02 0.1075 Inf
## other MALE elementary - latinx MALE middle 4.94e-02 0.1866 Inf
## other MALE elementary - black MALE middle 7.14e-02 0.1023 Inf
## other MALE elementary - other MALE middle 7.54e-02 0.1296 Inf
## other MALE elementary - white FEMALE middle 6.72e-02 0.1197 Inf
## other MALE elementary - latinx FEMALE middle -6.60e-01 0.3888 Inf
## other MALE elementary - black FEMALE middle 1.22e-01 0.1074 Inf
## other MALE elementary - other FEMALE middle 1.37e-02 0.1785 Inf
## other MALE elementary - white MALE high 3.55e-02 0.1225 Inf
## other MALE elementary - latinx MALE high 7.27e-03 0.2314 Inf
## other MALE elementary - black MALE high 1.01e-01 0.1132 Inf
## other MALE elementary - other MALE high -2.07e-01 0.1781 Inf
## other MALE elementary - white FEMALE high 8.75e-02 0.1433 Inf
## other MALE elementary - latinx FEMALE high 1.77e-01 0.2461 Inf
## other MALE elementary - black FEMALE high -5.65e-02 0.1206 Inf
## other MALE elementary - other FEMALE high 1.58e-02 0.2279 Inf
## white FEMALE elementary - latinx FEMALE elementary -2.03e-01 0.6010 Inf
## white FEMALE elementary - black FEMALE elementary 7.10e-03 0.1081 Inf
## white FEMALE elementary - other FEMALE elementary 1.09e-01 0.1766 Inf
## white FEMALE elementary - white MALE middle -2.23e-02 0.1018 Inf
## white FEMALE elementary - latinx MALE middle -6.58e-02 0.1834 Inf
## white FEMALE elementary - black MALE middle -4.37e-02 0.0964 Inf
## white FEMALE elementary - other MALE middle -3.97e-02 0.1249 Inf
## white FEMALE elementary - white FEMALE middle -4.80e-02 0.1146 Inf
## white FEMALE elementary - latinx FEMALE middle -7.75e-01 0.3872 Inf
## white FEMALE elementary - black FEMALE middle 7.18e-03 0.1017 Inf
## white FEMALE elementary - other FEMALE middle -1.01e-01 0.1752 Inf
## white FEMALE elementary - white MALE high -7.97e-02 0.1175 Inf
## white FEMALE elementary - latinx MALE high -1.08e-01 0.2288 Inf
## white FEMALE elementary - black MALE high -1.44e-02 0.1078 Inf
## white FEMALE elementary - other MALE high -3.22e-01 0.1747 Inf
## white FEMALE elementary - white FEMALE high -2.77e-02 0.1391 Inf
## white FEMALE elementary - latinx FEMALE high 6.16e-02 0.2436 Inf
## white FEMALE elementary - black FEMALE high -1.72e-01 0.1156 Inf
## white FEMALE elementary - other FEMALE high -9.94e-02 0.2252 Inf
## latinx FEMALE elementary - black FEMALE elementary 2.11e-01 0.5980 Inf
## latinx FEMALE elementary - other FEMALE elementary 3.12e-01 0.6140 Inf
## latinx FEMALE elementary - white MALE middle 1.81e-01 0.5969 Inf
## latinx FEMALE elementary - latinx MALE middle 1.38e-01 0.6160 Inf
## latinx FEMALE elementary - black MALE middle 1.60e-01 0.5959 Inf
## latinx FEMALE elementary - other MALE middle 1.64e-01 0.6012 Inf
## latinx FEMALE elementary - white FEMALE middle 1.56e-01 0.5992 Inf
## latinx FEMALE elementary - latinx FEMALE middle -5.72e-01 0.7042 Inf
## latinx FEMALE elementary - black FEMALE middle 2.11e-01 0.5968 Inf
## latinx FEMALE elementary - other FEMALE middle 1.02e-01 0.6136 Inf
## latinx FEMALE elementary - white MALE high 1.24e-01 0.5997 Inf
## latinx FEMALE elementary - latinx MALE high 9.56e-02 0.6310 Inf
## latinx FEMALE elementary - black MALE high 1.89e-01 0.5979 Inf
## latinx FEMALE elementary - other MALE high -1.19e-01 0.6135 Inf
## latinx FEMALE elementary - white FEMALE high 1.76e-01 0.6043 Inf
## latinx FEMALE elementary - latinx FEMALE high 2.65e-01 0.6366 Inf
## latinx FEMALE elementary - black FEMALE high 3.18e-02 0.5994 Inf
## latinx FEMALE elementary - other FEMALE high 1.04e-01 0.6298 Inf
## black FEMALE elementary - other FEMALE elementary 1.02e-01 0.1659 Inf
## black FEMALE elementary - white MALE middle -2.94e-02 0.0819 Inf
## black FEMALE elementary - latinx MALE middle -7.29e-02 0.1731 Inf
## black FEMALE elementary - black MALE middle -5.08e-02 0.0750 Inf
## black FEMALE elementary - other MALE middle -4.68e-02 0.1093 Inf
## black FEMALE elementary - white FEMALE middle -5.51e-02 0.0974 Inf
## black FEMALE elementary - latinx FEMALE middle -7.82e-01 0.3825 Inf
## black FEMALE elementary - black FEMALE middle 7.96e-05 0.0817 Inf
## black FEMALE elementary - other FEMALE middle -1.09e-01 0.1644 Inf
## black FEMALE elementary - white MALE high -8.68e-02 0.1008 Inf
## black FEMALE elementary - latinx MALE high -1.15e-01 0.2207 Inf
## black FEMALE elementary - black MALE high -2.15e-02 0.0892 Inf
## black FEMALE elementary - other MALE high -3.29e-01 0.1639 Inf
## black FEMALE elementary - white FEMALE high -3.48e-02 0.1252 Inf
## black FEMALE elementary - latinx FEMALE high 5.45e-02 0.2360 Inf
## black FEMALE elementary - black FEMALE high -1.79e-01 0.0985 Inf
## black FEMALE elementary - other FEMALE high -1.06e-01 0.2170 Inf
## other FEMALE elementary - white MALE middle -1.31e-01 0.1619 Inf
## other FEMALE elementary - latinx MALE middle -1.75e-01 0.2224 Inf
## other FEMALE elementary - black MALE middle -1.53e-01 0.1585 Inf
## other FEMALE elementary - other MALE middle -1.49e-01 0.1773 Inf
## other FEMALE elementary - white FEMALE middle -1.57e-01 0.1702 Inf
## other FEMALE elementary - latinx FEMALE middle -8.84e-01 0.4072 Inf
## other FEMALE elementary - black FEMALE middle -1.02e-01 0.1618 Inf
## other FEMALE elementary - other FEMALE middle -2.10e-01 0.2157 Inf
## other FEMALE elementary - white MALE high -1.88e-01 0.1722 Inf
## other FEMALE elementary - latinx MALE high -2.17e-01 0.2611 Inf
## other FEMALE elementary - black MALE high -1.23e-01 0.1657 Inf
## other FEMALE elementary - other MALE high -4.31e-01 0.2153 Inf
## other FEMALE elementary - white FEMALE high -1.36e-01 0.1875 Inf
## other FEMALE elementary - latinx FEMALE high -4.72e-02 0.2742 Inf
## other FEMALE elementary - black FEMALE high -2.81e-01 0.1709 Inf
## other FEMALE elementary - other FEMALE high -2.08e-01 0.2580 Inf
## white MALE middle - latinx MALE middle -4.35e-02 0.1692 Inf
## white MALE middle - black MALE middle -2.15e-02 0.0656 Inf
## white MALE middle - other MALE middle -1.74e-02 0.1031 Inf
## white MALE middle - white FEMALE middle -2.57e-02 0.0903 Inf
## white MALE middle - latinx FEMALE middle -7.53e-01 0.3808 Inf
## white MALE middle - black FEMALE middle 2.95e-02 0.0732 Inf
## white MALE middle - other FEMALE middle -7.91e-02 0.1603 Inf
## white MALE middle - white MALE high -5.74e-02 0.0940 Inf
## white MALE middle - latinx MALE high -8.56e-02 0.2176 Inf
## white MALE middle - black MALE high 7.92e-03 0.0814 Inf
## white MALE middle - other MALE high -3.00e-01 0.1598 Inf
## white MALE middle - white FEMALE high -5.38e-03 0.1198 Inf
## white MALE middle - latinx FEMALE high 8.38e-02 0.2332 Inf
## white MALE middle - black FEMALE high -1.49e-01 0.0915 Inf
## white MALE middle - other FEMALE high -7.71e-02 0.2139 Inf
## latinx MALE middle - black MALE middle 2.20e-02 0.1660 Inf
## latinx MALE middle - other MALE middle 2.60e-02 0.1841 Inf
## latinx MALE middle - white FEMALE middle 1.78e-02 0.1772 Inf
## latinx MALE middle - latinx FEMALE middle -7.09e-01 0.4102 Inf
## latinx MALE middle - black FEMALE middle 7.29e-02 0.1691 Inf
## latinx MALE middle - other FEMALE middle -3.57e-02 0.2213 Inf
## latinx MALE middle - white MALE high -1.39e-02 0.1791 Inf
## latinx MALE middle - latinx MALE high -4.21e-02 0.2658 Inf
## latinx MALE middle - black MALE high 5.14e-02 0.1729 Inf
## latinx MALE middle - other MALE high -2.56e-01 0.2209 Inf
## latinx MALE middle - white FEMALE high 3.81e-02 0.1939 Inf
## latinx MALE middle - latinx FEMALE high 1.27e-01 0.2786 Inf
## latinx MALE middle - black FEMALE high -1.06e-01 0.1779 Inf
## latinx MALE middle - other FEMALE high -3.36e-02 0.2627 Inf
## black MALE middle - other MALE middle 4.03e-03 0.0977 Inf
## black MALE middle - white FEMALE middle -4.21e-03 0.0841 Inf
## black MALE middle - latinx FEMALE middle -7.31e-01 0.3793 Inf
## black MALE middle - black FEMALE middle 5.09e-02 0.0653 Inf
## black MALE middle - other FEMALE middle -5.77e-02 0.1569 Inf
## black MALE middle - white MALE high -3.59e-02 0.0880 Inf
## black MALE middle - latinx MALE high -6.41e-02 0.2151 Inf
## black MALE middle - black MALE high 2.94e-02 0.0745 Inf
## black MALE middle - other MALE high -2.79e-01 0.1564 Inf
## black MALE middle - white FEMALE high 1.61e-02 0.1152 Inf
## black MALE middle - latinx FEMALE high 1.05e-01 0.2308 Inf
## black MALE middle - black FEMALE high -1.28e-01 0.0854 Inf
## black MALE middle - other FEMALE high -5.56e-02 0.2113 Inf
## other MALE middle - white FEMALE middle -8.25e-03 0.1157 Inf
## other MALE middle - latinx FEMALE middle -7.35e-01 0.3876 Inf
## other MALE middle - black FEMALE middle 4.69e-02 0.1029 Inf
## other MALE middle - other FEMALE middle -6.17e-02 0.1759 Inf
## other MALE middle - white MALE high -3.99e-02 0.1186 Inf
## other MALE middle - latinx MALE high -6.82e-02 0.2294 Inf
## other MALE middle - black MALE high 2.53e-02 0.1090 Inf
## other MALE middle - other MALE high -2.83e-01 0.1755 Inf
## other MALE middle - white FEMALE high 1.21e-02 0.1400 Inf
## other MALE middle - latinx FEMALE high 1.01e-01 0.2442 Inf
## other MALE middle - black FEMALE high -1.32e-01 0.1167 Inf
## other MALE middle - other FEMALE high -5.96e-02 0.2258 Inf
## white FEMALE middle - latinx FEMALE middle -7.27e-01 0.3844 Inf
## white FEMALE middle - black FEMALE middle 5.51e-02 0.0901 Inf
## white FEMALE middle - other FEMALE middle -5.35e-02 0.1687 Inf
## white FEMALE middle - white MALE high -3.17e-02 0.1077 Inf
## white FEMALE middle - latinx MALE high -5.99e-02 0.2239 Inf
## white FEMALE middle - black MALE high 3.36e-02 0.0970 Inf
## white FEMALE middle - other MALE high -2.74e-01 0.1683 Inf
## white FEMALE middle - white FEMALE high 2.03e-02 0.1308 Inf
## white FEMALE middle - latinx FEMALE high 1.10e-01 0.2391 Inf
## white FEMALE middle - black FEMALE high -1.24e-01 0.1056 Inf
## white FEMALE middle - other FEMALE high -5.14e-02 0.2203 Inf
## latinx FEMALE middle - black FEMALE middle 7.82e-01 0.3807 Inf
## latinx FEMALE middle - other FEMALE middle 6.74e-01 0.4066 Inf
## latinx FEMALE middle - white MALE high 6.95e-01 0.3853 Inf
## latinx FEMALE middle - latinx MALE high 6.67e-01 0.4324 Inf
## latinx FEMALE middle - black MALE high 7.61e-01 0.3824 Inf
## latinx FEMALE middle - other MALE high 4.53e-01 0.4064 Inf
## latinx FEMALE middle - white FEMALE high 7.47e-01 0.3923 Inf
## latinx FEMALE middle - latinx FEMALE high 8.37e-01 0.4404 Inf
## latinx FEMALE middle - black FEMALE high 6.03e-01 0.3847 Inf
## latinx FEMALE middle - other FEMALE high 6.76e-01 0.4305 Inf
## black FEMALE middle - other FEMALE middle -1.09e-01 0.1602 Inf
## black FEMALE middle - white MALE high -8.68e-02 0.0938 Inf
## black FEMALE middle - latinx MALE high -1.15e-01 0.2176 Inf
## black FEMALE middle - black MALE high -2.15e-02 0.0812 Inf
## black FEMALE middle - other MALE high -3.29e-01 0.1597 Inf
## black FEMALE middle - white FEMALE high -3.48e-02 0.1197 Inf
## black FEMALE middle - latinx FEMALE high 5.44e-02 0.2331 Inf
## black FEMALE middle - black FEMALE high -1.79e-01 0.0914 Inf
## black FEMALE middle - other FEMALE high -1.07e-01 0.2138 Inf
## other FEMALE middle - white MALE high 2.18e-02 0.1707 Inf
## other FEMALE middle - latinx MALE high -6.48e-03 0.2602 Inf
## other FEMALE middle - black MALE high 8.71e-02 0.1642 Inf
## other FEMALE middle - other MALE high -2.21e-01 0.2142 Inf
## other FEMALE middle - white FEMALE high 7.38e-02 0.1862 Inf
## other FEMALE middle - latinx FEMALE high 1.63e-01 0.2733 Inf
## other FEMALE middle - black FEMALE high -7.03e-02 0.1694 Inf
## other FEMALE middle - other FEMALE high 2.06e-03 0.2570 Inf
## white MALE high - latinx MALE high -2.82e-02 0.2254 Inf
## white MALE high - black MALE high 6.53e-02 0.1004 Inf
## white MALE high - other MALE high -2.43e-01 0.1703 Inf
## white MALE high - white FEMALE high 5.20e-02 0.1334 Inf
## white MALE high - latinx FEMALE high 1.41e-01 0.2405 Inf
## white MALE high - black FEMALE high -9.20e-02 0.1087 Inf
## white MALE high - other FEMALE high -1.97e-02 0.2218 Inf
## latinx MALE high - black MALE high 9.35e-02 0.2205 Inf
## latinx MALE high - other MALE high -2.14e-01 0.2599 Inf
## latinx MALE high - white FEMALE high 8.02e-02 0.2373 Inf
## latinx MALE high - latinx FEMALE high 1.69e-01 0.3104 Inf
## latinx MALE high - black FEMALE high -6.38e-02 0.2244 Inf
## latinx MALE high - other FEMALE high 8.54e-03 0.2962 Inf
## black MALE high - other MALE high -3.08e-01 0.1637 Inf
## black MALE high - white FEMALE high -1.33e-02 0.1249 Inf
## black MALE high - latinx FEMALE high 7.59e-02 0.2359 Inf
## black MALE high - black FEMALE high -1.57e-01 0.0981 Inf
## black MALE high - other FEMALE high -8.50e-02 0.2168 Inf
## other MALE high - white FEMALE high 2.95e-01 0.1858 Inf
## other MALE high - latinx FEMALE high 3.84e-01 0.2730 Inf
## other MALE high - black FEMALE high 1.51e-01 0.1690 Inf
## other MALE high - other FEMALE high 2.23e-01 0.2567 Inf
## white FEMALE high - latinx FEMALE high 8.92e-02 0.2517 Inf
## white FEMALE high - black FEMALE high -1.44e-01 0.1317 Inf
## white FEMALE high - other FEMALE high -7.17e-02 0.2339 Inf
## latinx FEMALE high - black FEMALE high -2.33e-01 0.2395 Inf
## latinx FEMALE high - other FEMALE high -1.61e-01 0.3078 Inf
## black FEMALE high - other FEMALE high 7.23e-02 0.2208 Inf
## z.ratio p.value
## -0.515 0.6064
## -1.492 0.1358
## -1.391 0.1641
## -0.362 0.7170
## -0.404 0.6862
## -0.361 0.7178
## 0.434 0.6644
## -0.782 0.4344
## -0.607 0.5441
## -1.174 0.2404
## -0.734 0.4628
## -0.921 0.3573
## -2.131 0.0331
## -0.400 0.6893
## -0.860 0.3899
## -1.214 0.2246
## -0.666 0.5055
## -0.617 0.5374
## -2.230 0.0258
## -0.537 0.5913
## 0.101 0.9194
## -2.219 0.0265
## -0.638 0.5236
## 0.017 0.9861
## -0.157 0.8752
## 0.332 0.7396
## -0.198 0.8428
## 0.375 0.7074
## 0.702 0.4828
## 0.249 0.8034
## 0.045 0.9641
## 0.155 0.8771
## 0.163 0.8708
## 0.131 0.8961
## -1.601 0.1094
## 0.381 0.7033
## -0.089 0.9295
## -0.007 0.9941
## -0.099 0.9213
## 0.281 0.7788
## -0.922 0.3564
## 0.207 0.8359
## 0.443 0.6575
## -0.407 0.6838
## -0.071 0.9432
## -0.387 0.6986
## 0.738 0.4608
## -0.217 0.8283
## 1.013 0.3111
## 1.136 0.2560
## 0.725 0.4683
## 0.049 0.9607
## 0.479 0.6321
## 0.338 0.7355
## 0.294 0.7686
## -1.843 0.0653
## 1.142 0.2536
## -0.172 0.8637
## -0.061 0.9517
## -0.156 0.8761
## 0.749 0.4536
## -1.562 0.1184
## 0.391 0.6956
## 0.583 0.5598
## -1.086 0.2777
## -0.119 0.9055
## 0.895 0.3706
## -0.147 0.8834
## 1.077 0.2814
## 1.245 0.2133
## 0.864 0.3877
## 0.265 0.7912
## 0.698 0.4853
## 0.582 0.5605
## 0.561 0.5746
## -1.697 0.0896
## 1.139 0.2545
## 0.077 0.9386
## 0.290 0.7719
## 0.031 0.9749
## 0.891 0.3731
## -1.163 0.2450
## 0.611 0.5414
## 0.718 0.4727
## -0.469 0.6394
## 0.069 0.9447
## -0.339 0.7349
## 0.066 0.9477
## 0.616 0.5378
## -0.219 0.8268
## -0.359 0.7199
## -0.454 0.6498
## -0.318 0.7506
## -0.418 0.6757
## -2.002 0.0453
## 0.071 0.9437
## -0.579 0.5626
## -0.678 0.4980
## -0.472 0.6372
## -0.133 0.8940
## -1.844 0.0652
## -0.199 0.8423
## 0.253 0.8005
## -1.485 0.1375
## -0.441 0.6591
## 0.352 0.7247
## 0.509 0.6111
## 0.304 0.7614
## 0.224 0.8231
## 0.268 0.7887
## 0.272 0.7853
## 0.260 0.7952
## -0.812 0.4169
## 0.353 0.7241
## 0.166 0.8679
## 0.206 0.8364
## 0.151 0.8796
## 0.316 0.7518
## -0.194 0.8465
## 0.291 0.7711
## 0.416 0.6772
## 0.053 0.9577
## 0.165 0.8687
## 0.613 0.5399
## -0.359 0.7198
## -0.421 0.6738
## -0.678 0.4976
## -0.428 0.6685
## -0.566 0.5717
## -2.045 0.0409
## 0.001 0.9992
## -0.660 0.5092
## -0.861 0.3892
## -0.521 0.6023
## -0.241 0.8098
## -2.009 0.0445
## -0.278 0.7813
## 0.231 0.8175
## -1.815 0.0695
## -0.491 0.6237
## -0.810 0.4180
## -0.785 0.4325
## -0.963 0.3357
## -0.838 0.4023
## -0.921 0.3571
## -2.171 0.0299
## -0.628 0.5299
## -0.975 0.3297
## -1.095 0.2737
## -0.830 0.4066
## -0.743 0.4572
## -2.002 0.0453
## -0.728 0.4668
## -0.172 0.8632
## -1.642 0.1007
## -0.807 0.4198
## -0.257 0.7973
## -0.327 0.7434
## -0.169 0.8657
## -0.284 0.7762
## -1.977 0.0480
## 0.403 0.6872
## -0.494 0.6216
## -0.611 0.5415
## -0.393 0.6941
## 0.097 0.9225
## -1.877 0.0606
## -0.045 0.9642
## 0.360 0.7192
## -1.632 0.1027
## -0.360 0.7186
## 0.133 0.8945
## 0.142 0.8875
## 0.100 0.9200
## -1.729 0.0837
## 0.431 0.6663
## -0.161 0.8720
## -0.078 0.9382
## -0.159 0.8740
## 0.297 0.7662
## -1.161 0.2456
## 0.196 0.8442
## 0.457 0.6477
## -0.596 0.5515
## -0.128 0.8982
## 0.041 0.9671
## -0.050 0.9600
## -1.928 0.0539
## 0.780 0.4355
## -0.368 0.7132
## -0.408 0.6832
## -0.298 0.7656
## 0.395 0.6932
## -1.781 0.0750
## 0.140 0.8889
## 0.456 0.6482
## -1.498 0.1340
## -0.263 0.7924
## -0.071 0.9432
## -1.897 0.0578
## 0.456 0.6487
## -0.351 0.7258
## -0.337 0.7363
## -0.297 0.7663
## 0.233 0.8161
## -1.610 0.1074
## 0.086 0.9314
## 0.415 0.6783
## -1.131 0.2581
## -0.264 0.7917
## -1.892 0.0585
## 0.612 0.5407
## -0.317 0.7514
## -0.294 0.7685
## -0.268 0.7890
## 0.346 0.7290
## -1.630 0.1031
## 0.155 0.8767
## 0.458 0.6468
## -1.172 0.2413
## -0.233 0.8155
## 2.055 0.0399
## 1.657 0.0975
## 1.805 0.0711
## 1.543 0.1228
## 1.989 0.0467
## 1.114 0.2651
## 1.905 0.0568
## 1.900 0.0575
## 1.569 0.1167
## 1.570 0.1165
## -0.678 0.4979
## -0.926 0.3545
## -0.529 0.5969
## -0.265 0.7909
## -2.062 0.0392
## -0.291 0.7709
## 0.233 0.8155
## -1.958 0.0503
## -0.498 0.6183
## 0.127 0.8986
## -0.025 0.9801
## 0.530 0.5959
## -1.031 0.3025
## 0.396 0.6920
## 0.596 0.5509
## -0.415 0.6782
## 0.008 0.9936
## -0.125 0.9003
## 0.650 0.5154
## -1.425 0.1542
## 0.390 0.6967
## 0.587 0.5570
## -0.846 0.3973
## -0.089 0.9292
## 0.424 0.6714
## -0.825 0.4095
## 0.338 0.7353
## 0.546 0.5851
## -0.284 0.7762
## 0.029 0.9770
## -1.881 0.0600
## -0.106 0.9152
## 0.322 0.7475
## -1.603 0.1088
## -0.392 0.6950
## 1.586 0.1128
## 1.406 0.1598
## 0.891 0.3729
## 0.868 0.3853
## 0.355 0.7229
## -1.094 0.2741
## -0.307 0.7592
## -0.974 0.3301
## -0.523 0.6011
## 0.328 0.7432Neuroticism
neuro <- glm(ever_suspended ~ race * sex * neuro_tipi * school_group,
family = binomial, data = nlsy_l)
summary(neuro)##
## Call:
## glm(formula = ever_suspended ~ race * sex * neuro_tipi * school_group,
## family = binomial, data = nlsy_l)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2585 -0.6885 -0.5172 -0.3508 2.6829
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.227271 0.259840 -0.875
## racelatinx -2.588054 1.281912 -2.019
## raceblack 0.519466 0.349131 1.488
## raceother -0.186046 0.540705 -0.344
## sexFEMALE -1.868422 0.432733 -4.318
## neuro_tipi -0.304872 0.052066 -5.855
## school_groupmiddle -0.092287 0.354270 -0.260
## school_grouphigh 0.141094 0.448613 0.315
## racelatinx:sexFEMALE -1.169534 3.894039 -0.300
## raceblack:sexFEMALE 1.247166 0.565059 2.207
## raceother:sexFEMALE -0.488508 0.929350 -0.526
## racelatinx:neuro_tipi 0.605852 0.224885 2.694
## raceblack:neuro_tipi 0.201293 0.069092 2.913
## raceother:neuro_tipi 0.179183 0.104826 1.709
## sexFEMALE:neuro_tipi 0.172533 0.089423 1.929
## racelatinx:school_groupmiddle 2.526387 1.502680 1.681
## raceblack:school_groupmiddle 0.004981 0.464942 0.011
## raceother:school_groupmiddle 0.145720 0.719880 0.202
## racelatinx:school_grouphigh 1.207578 1.676614 0.720
## raceblack:school_grouphigh -0.360812 0.590638 -0.611
## raceother:school_grouphigh -0.205118 0.950680 -0.216
## sexFEMALE:school_groupmiddle -0.020834 0.581364 -0.036
## sexFEMALE:school_grouphigh 1.033845 0.694835 1.488
## neuro_tipi:school_groupmiddle 0.075578 0.070243 1.076
## neuro_tipi:school_grouphigh 0.083044 0.088835 0.935
## racelatinx:sexFEMALE:neuro_tipi -0.017126 0.669354 -0.026
## raceblack:sexFEMALE:neuro_tipi -0.300338 0.116688 -2.574
## raceother:sexFEMALE:neuro_tipi 0.033174 0.183071 0.181
## racelatinx:sexFEMALE:school_groupmiddle -1.690932 4.330173 -0.390
## raceblack:sexFEMALE:school_groupmiddle -0.763715 0.745576 -1.024
## raceother:sexFEMALE:school_groupmiddle 0.013332 1.280943 0.010
## racelatinx:sexFEMALE:school_grouphigh 2.200151 4.187369 0.525
## raceblack:sexFEMALE:school_grouphigh -0.833700 0.898918 -0.927
## raceother:sexFEMALE:school_grouphigh -0.091235 1.542572 -0.059
## racelatinx:neuro_tipi:school_groupmiddle -0.527982 0.268906 -1.963
## raceblack:neuro_tipi:school_groupmiddle -0.124226 0.091433 -1.359
## raceother:neuro_tipi:school_groupmiddle -0.074179 0.139926 -0.530
## racelatinx:neuro_tipi:school_grouphigh -0.259985 0.302640 -0.859
## raceblack:neuro_tipi:school_grouphigh -0.065550 0.115492 -0.568
## raceother:neuro_tipi:school_grouphigh -0.027810 0.181328 -0.153
## sexFEMALE:neuro_tipi:school_groupmiddle 0.044746 0.118458 0.378
## sexFEMALE:neuro_tipi:school_grouphigh -0.194266 0.143736 -1.352
## racelatinx:sexFEMALE:neuro_tipi:school_groupmiddle 0.431323 0.753694 0.572
## raceblack:sexFEMALE:neuro_tipi:school_groupmiddle 0.176402 0.152075 1.160
## raceother:sexFEMALE:neuro_tipi:school_groupmiddle -0.047772 0.252376 -0.189
## racelatinx:sexFEMALE:neuro_tipi:school_grouphigh -0.131441 0.732909 -0.179
## raceblack:sexFEMALE:neuro_tipi:school_grouphigh 0.234566 0.184356 1.272
## raceother:sexFEMALE:neuro_tipi:school_grouphigh 0.050954 0.303259 0.168
## Pr(>|z|)
## (Intercept) 0.38176
## racelatinx 0.04350 *
## raceblack 0.13678
## raceother 0.73079
## sexFEMALE 1.58e-05 ***
## neuro_tipi 4.76e-09 ***
## school_groupmiddle 0.79448
## school_grouphigh 0.75313
## racelatinx:sexFEMALE 0.76392
## raceblack:sexFEMALE 0.02730 *
## raceother:sexFEMALE 0.59913
## racelatinx:neuro_tipi 0.00706 **
## raceblack:neuro_tipi 0.00357 **
## raceother:neuro_tipi 0.08739 .
## sexFEMALE:neuro_tipi 0.05368 .
## racelatinx:school_groupmiddle 0.09271 .
## raceblack:school_groupmiddle 0.99145
## raceother:school_groupmiddle 0.83959
## racelatinx:school_grouphigh 0.47137
## raceblack:school_grouphigh 0.54128
## raceother:school_grouphigh 0.82918
## sexFEMALE:school_groupmiddle 0.97141
## sexFEMALE:school_grouphigh 0.13678
## neuro_tipi:school_groupmiddle 0.28195
## neuro_tipi:school_grouphigh 0.34989
## racelatinx:sexFEMALE:neuro_tipi 0.97959
## raceblack:sexFEMALE:neuro_tipi 0.01006 *
## raceother:sexFEMALE:neuro_tipi 0.85620
## racelatinx:sexFEMALE:school_groupmiddle 0.69617
## raceblack:sexFEMALE:school_groupmiddle 0.30568
## raceother:sexFEMALE:school_groupmiddle 0.99170
## racelatinx:sexFEMALE:school_grouphigh 0.59929
## raceblack:sexFEMALE:school_grouphigh 0.35369
## raceother:sexFEMALE:school_grouphigh 0.95284
## racelatinx:neuro_tipi:school_groupmiddle 0.04959 *
## raceblack:neuro_tipi:school_groupmiddle 0.17426
## raceother:neuro_tipi:school_groupmiddle 0.59602
## racelatinx:neuro_tipi:school_grouphigh 0.39031
## raceblack:neuro_tipi:school_grouphigh 0.57033
## raceother:neuro_tipi:school_grouphigh 0.87811
## sexFEMALE:neuro_tipi:school_groupmiddle 0.70563
## sexFEMALE:neuro_tipi:school_grouphigh 0.17652
## racelatinx:sexFEMALE:neuro_tipi:school_groupmiddle 0.56713
## raceblack:sexFEMALE:neuro_tipi:school_groupmiddle 0.24606
## raceother:sexFEMALE:neuro_tipi:school_groupmiddle 0.84987
## racelatinx:sexFEMALE:neuro_tipi:school_grouphigh 0.85767
## raceblack:sexFEMALE:neuro_tipi:school_grouphigh 0.20325
## raceother:sexFEMALE:neuro_tipi:school_grouphigh 0.86657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 18409 on 18336 degrees of freedom
## Residual deviance: 16860 on 18289 degrees of freedom
## AIC: 16956
##
## Number of Fisher Scoring iterations: 6plot_model(neuro, type = "pred", terms = c("neuro_tipi", "race", "sex", "school_group"))
object1 <- emtrends(neuro, pairwise ~ race * sex * school_group, var = "neuro_tipi", adjust = "none")
object1$contrasts## contrast estimate SE df
## white MALE elementary - latinx MALE elementary -0.605851 0.2249 Inf
## white MALE elementary - black MALE elementary -0.201293 0.0691 Inf
## white MALE elementary - other MALE elementary -0.179183 0.1048 Inf
## white MALE elementary - white FEMALE elementary -0.172533 0.0894 Inf
## white MALE elementary - latinx FEMALE elementary -0.761259 0.6284 Inf
## white MALE elementary - black FEMALE elementary -0.073488 0.0792 Inf
## white MALE elementary - other FEMALE elementary -0.384890 0.1413 Inf
## white MALE elementary - white MALE middle -0.075578 0.0702 Inf
## white MALE elementary - latinx MALE middle -0.153448 0.1491 Inf
## white MALE elementary - black MALE middle -0.152645 0.0638 Inf
## white MALE elementary - other MALE middle -0.180582 0.0953 Inf
## white MALE elementary - white FEMALE middle -0.292858 0.0808 Inf
## white MALE elementary - latinx FEMALE middle -0.784925 0.3117 Inf
## white MALE elementary - black FEMALE middle -0.245989 0.0694 Inf
## white MALE elementary - other FEMALE middle -0.383263 0.1431 Inf
## white MALE elementary - white MALE high -0.083044 0.0888 Inf
## white MALE elementary - latinx MALE high -0.428910 0.1963 Inf
## white MALE elementary - black MALE high -0.218787 0.0781 Inf
## white MALE elementary - other MALE high -0.234417 0.1394 Inf
## white MALE elementary - white FEMALE high -0.061311 0.1010 Inf
## white MALE elementary - latinx FEMALE high -0.258611 0.2082 Inf
## white MALE elementary - black FEMALE high -0.131282 0.0839 Inf
## white MALE elementary - other FEMALE high -0.296812 0.1783 Inf
## latinx MALE elementary - black MALE elementary 0.404559 0.2234 Inf
## latinx MALE elementary - other MALE elementary 0.426669 0.2369 Inf
## latinx MALE elementary - white FEMALE elementary 0.433318 0.2305 Inf
## latinx MALE elementary - latinx FEMALE elementary -0.155407 0.6634 Inf
## latinx MALE elementary - black FEMALE elementary 0.532363 0.2268 Inf
## latinx MALE elementary - other FEMALE elementary 0.220962 0.2552 Inf
## latinx MALE elementary - white MALE middle 0.530273 0.2238 Inf
## latinx MALE elementary - latinx MALE middle 0.452403 0.2596 Inf
## latinx MALE elementary - black MALE middle 0.453206 0.2219 Inf
## latinx MALE elementary - other MALE middle 0.425270 0.2329 Inf
## latinx MALE elementary - white FEMALE middle 0.312994 0.2273 Inf
## latinx MALE elementary - latinx FEMALE middle -0.179073 0.3773 Inf
## latinx MALE elementary - black FEMALE middle 0.359862 0.2235 Inf
## latinx MALE elementary - other FEMALE middle 0.222589 0.2562 Inf
## latinx MALE elementary - white MALE high 0.522808 0.2303 Inf
## latinx MALE elementary - latinx MALE high 0.176942 0.2893 Inf
## latinx MALE elementary - black MALE high 0.387065 0.2264 Inf
## latinx MALE elementary - other MALE high 0.371435 0.2541 Inf
## latinx MALE elementary - white FEMALE high 0.544541 0.2353 Inf
## latinx MALE elementary - latinx FEMALE high 0.347241 0.2975 Inf
## latinx MALE elementary - black FEMALE high 0.474570 0.2284 Inf
## latinx MALE elementary - other FEMALE high 0.309040 0.2774 Inf
## black MALE elementary - other MALE elementary 0.022110 0.1017 Inf
## black MALE elementary - white FEMALE elementary 0.028760 0.0857 Inf
## black MALE elementary - latinx FEMALE elementary -0.559966 0.6279 Inf
## black MALE elementary - black FEMALE elementary 0.127805 0.0750 Inf
## black MALE elementary - other FEMALE elementary -0.183597 0.1389 Inf
## black MALE elementary - white MALE middle 0.125714 0.0655 Inf
## black MALE elementary - latinx MALE middle 0.047845 0.1469 Inf
## black MALE elementary - black MALE middle 0.048647 0.0585 Inf
## black MALE elementary - other MALE middle 0.020711 0.0918 Inf
## black MALE elementary - white FEMALE middle -0.091565 0.0767 Inf
## black MALE elementary - latinx FEMALE middle -0.583632 0.3107 Inf
## black MALE elementary - black FEMALE middle -0.044697 0.0646 Inf
## black MALE elementary - other FEMALE middle -0.181970 0.1408 Inf
## black MALE elementary - white MALE high 0.118249 0.0851 Inf
## black MALE elementary - latinx MALE high -0.227617 0.1947 Inf
## black MALE elementary - black MALE high -0.017494 0.0738 Inf
## black MALE elementary - other MALE high -0.033124 0.1370 Inf
## black MALE elementary - white FEMALE high 0.139982 0.0977 Inf
## black MALE elementary - latinx FEMALE high -0.057318 0.2066 Inf
## black MALE elementary - black FEMALE high 0.070011 0.0799 Inf
## black MALE elementary - other FEMALE high -0.095519 0.1765 Inf
## other MALE elementary - white FEMALE elementary 0.006650 0.1165 Inf
## other MALE elementary - latinx FEMALE elementary -0.582076 0.6328 Inf
## other MALE elementary - black FEMALE elementary 0.105695 0.1088 Inf
## other MALE elementary - other FEMALE elementary -0.205707 0.1597 Inf
## other MALE elementary - white MALE middle 0.103604 0.1025 Inf
## other MALE elementary - latinx MALE middle 0.025735 0.1667 Inf
## other MALE elementary - black MALE middle 0.026537 0.0982 Inf
## other MALE elementary - other MALE middle -0.001399 0.1210 Inf
## other MALE elementary - white FEMALE middle -0.113675 0.1100 Inf
## other MALE elementary - latinx FEMALE middle -0.605742 0.3205 Inf
## other MALE elementary - black FEMALE middle -0.066806 0.1019 Inf
## other MALE elementary - other FEMALE middle -0.204080 0.1614 Inf
## other MALE elementary - white MALE high 0.096139 0.1160 Inf
## other MALE elementary - latinx MALE high -0.249727 0.2100 Inf
## other MALE elementary - black MALE high -0.039604 0.1080 Inf
## other MALE elementary - other MALE high -0.055234 0.1581 Inf
## other MALE elementary - white FEMALE high 0.117872 0.1255 Inf
## other MALE elementary - latinx FEMALE high -0.079428 0.2211 Inf
## other MALE elementary - black FEMALE high 0.047901 0.1123 Inf
## other MALE elementary - other FEMALE high -0.117629 0.1933 Inf
## white FEMALE elementary - latinx FEMALE elementary -0.588726 0.6304 Inf
## white FEMALE elementary - black FEMALE elementary 0.099045 0.0940 Inf
## white FEMALE elementary - other FEMALE elementary -0.212357 0.1501 Inf
## white FEMALE elementary - white MALE middle 0.096955 0.0867 Inf
## white FEMALE elementary - latinx MALE middle 0.019085 0.1575 Inf
## white FEMALE elementary - black MALE middle 0.019888 0.0815 Inf
## white FEMALE elementary - other MALE middle -0.008049 0.1079 Inf
## white FEMALE elementary - white FEMALE middle -0.120325 0.0954 Inf
## white FEMALE elementary - latinx FEMALE middle -0.612391 0.3158 Inf
## white FEMALE elementary - black FEMALE middle -0.073456 0.0860 Inf
## white FEMALE elementary - other FEMALE middle -0.210730 0.1519 Inf
## white FEMALE elementary - white MALE high 0.089489 0.1023 Inf
## white FEMALE elementary - latinx MALE high -0.256377 0.2028 Inf
## white FEMALE elementary - black MALE high -0.046254 0.0931 Inf
## white FEMALE elementary - other MALE high -0.061884 0.1483 Inf
## white FEMALE elementary - white FEMALE high 0.111222 0.1130 Inf
## white FEMALE elementary - latinx FEMALE high -0.086078 0.2143 Inf
## white FEMALE elementary - black FEMALE high 0.041251 0.0980 Inf
## white FEMALE elementary - other FEMALE high -0.124279 0.1854 Inf
## latinx FEMALE elementary - black FEMALE elementary 0.687771 0.6291 Inf
## latinx FEMALE elementary - other FEMALE elementary 0.376369 0.6399 Inf
## latinx FEMALE elementary - white MALE middle 0.685680 0.6280 Inf
## latinx FEMALE elementary - latinx MALE middle 0.607811 0.6416 Inf
## latinx FEMALE elementary - black MALE middle 0.608613 0.6273 Inf
## latinx FEMALE elementary - other MALE middle 0.580677 0.6313 Inf
## latinx FEMALE elementary - white FEMALE middle 0.468401 0.6293 Inf
## latinx FEMALE elementary - latinx FEMALE middle -0.023666 0.6976 Inf
## latinx FEMALE elementary - black FEMALE middle 0.515270 0.6279 Inf
## latinx FEMALE elementary - other FEMALE middle 0.377996 0.6403 Inf
## latinx FEMALE elementary - white MALE high 0.678215 0.6304 Inf
## latinx FEMALE elementary - latinx MALE high 0.332349 0.6542 Inf
## latinx FEMALE elementary - black MALE high 0.542472 0.6289 Inf
## latinx FEMALE elementary - other MALE high 0.526842 0.6394 Inf
## latinx FEMALE elementary - white FEMALE high 0.699948 0.6322 Inf
## latinx FEMALE elementary - latinx FEMALE high 0.502648 0.6579 Inf
## latinx FEMALE elementary - black FEMALE high 0.629977 0.6297 Inf
## latinx FEMALE elementary - other FEMALE high 0.464447 0.6490 Inf
## black FEMALE elementary - other FEMALE elementary -0.311402 0.1442 Inf
## black FEMALE elementary - white MALE middle -0.002090 0.0760 Inf
## black FEMALE elementary - latinx MALE middle -0.079960 0.1519 Inf
## black FEMALE elementary - black MALE middle -0.079157 0.0701 Inf
## black FEMALE elementary - other MALE middle -0.107094 0.0996 Inf
## black FEMALE elementary - white FEMALE middle -0.219369 0.0858 Inf
## black FEMALE elementary - latinx FEMALE middle -0.711436 0.3131 Inf
## black FEMALE elementary - black FEMALE middle -0.172501 0.0753 Inf
## black FEMALE elementary - other FEMALE middle -0.309775 0.1461 Inf
## black FEMALE elementary - white MALE high -0.009556 0.0935 Inf
## black FEMALE elementary - latinx MALE high -0.355422 0.1985 Inf
## black FEMALE elementary - black MALE high -0.145299 0.0833 Inf
## black FEMALE elementary - other MALE high -0.160929 0.1424 Inf
## black FEMALE elementary - white FEMALE high 0.012177 0.1051 Inf
## black FEMALE elementary - latinx FEMALE high -0.185122 0.2102 Inf
## black FEMALE elementary - black FEMALE high -0.057794 0.0888 Inf
## black FEMALE elementary - other FEMALE high -0.223323 0.1806 Inf
## other FEMALE elementary - white MALE middle 0.309311 0.1395 Inf
## other FEMALE elementary - latinx MALE middle 0.231441 0.1917 Inf
## other FEMALE elementary - black MALE middle 0.232244 0.1364 Inf
## other FEMALE elementary - other MALE middle 0.204308 0.1537 Inf
## other FEMALE elementary - white FEMALE middle 0.092032 0.1451 Inf
## other FEMALE elementary - latinx FEMALE middle -0.400035 0.3342 Inf
## other FEMALE elementary - black FEMALE middle 0.138901 0.1391 Inf
## other FEMALE elementary - other FEMALE middle 0.001627 0.1871 Inf
## other FEMALE elementary - white MALE high 0.301846 0.1497 Inf
## other FEMALE elementary - latinx MALE high -0.044020 0.2304 Inf
## other FEMALE elementary - black MALE high 0.166103 0.1436 Inf
## other FEMALE elementary - other MALE high 0.150473 0.1843 Inf
## other FEMALE elementary - white FEMALE high 0.323579 0.1572 Inf
## other FEMALE elementary - latinx FEMALE high 0.126279 0.2405 Inf
## other FEMALE elementary - black FEMALE high 0.253608 0.1468 Inf
## other FEMALE elementary - other FEMALE high 0.088078 0.2152 Inf
## white MALE middle - latinx MALE middle -0.077870 0.1474 Inf
## white MALE middle - black MALE middle -0.077067 0.0599 Inf
## white MALE middle - other MALE middle -0.105003 0.0927 Inf
## white MALE middle - white FEMALE middle -0.217279 0.0777 Inf
## white MALE middle - latinx FEMALE middle -0.709346 0.3110 Inf
## white MALE middle - black FEMALE middle -0.170411 0.0658 Inf
## white MALE middle - other FEMALE middle -0.307684 0.1414 Inf
## white MALE middle - white MALE high -0.007465 0.0860 Inf
## white MALE middle - latinx MALE high -0.353332 0.1951 Inf
## white MALE middle - black MALE high -0.143208 0.0749 Inf
## white MALE middle - other MALE high -0.158839 0.1376 Inf
## white MALE middle - white FEMALE high 0.014268 0.0985 Inf
## white MALE middle - latinx FEMALE high -0.183032 0.2070 Inf
## white MALE middle - black FEMALE high -0.055703 0.0809 Inf
## white MALE middle - other FEMALE high -0.221233 0.1769 Inf
## latinx MALE middle - black MALE middle 0.000803 0.1445 Inf
## latinx MALE middle - other MALE middle -0.027134 0.1609 Inf
## latinx MALE middle - white FEMALE middle -0.139409 0.1527 Inf
## latinx MALE middle - latinx FEMALE middle -0.631476 0.3376 Inf
## latinx MALE middle - black FEMALE middle -0.092541 0.1471 Inf
## latinx MALE middle - other FEMALE middle -0.229815 0.1931 Inf
## latinx MALE middle - white MALE high 0.070404 0.1571 Inf
## latinx MALE middle - latinx MALE high -0.275462 0.2353 Inf
## latinx MALE middle - black MALE high -0.065339 0.1513 Inf
## latinx MALE middle - other MALE high -0.080969 0.1903 Inf
## latinx MALE middle - white FEMALE high 0.092137 0.1643 Inf
## latinx MALE middle - latinx FEMALE high -0.105162 0.2452 Inf
## latinx MALE middle - black FEMALE high 0.022166 0.1544 Inf
## latinx MALE middle - other FEMALE high -0.143363 0.2204 Inf
## black MALE middle - other MALE middle -0.027937 0.0879 Inf
## black MALE middle - white FEMALE middle -0.140212 0.0719 Inf
## black MALE middle - latinx FEMALE middle -0.632279 0.3096 Inf
## black MALE middle - black FEMALE middle -0.093344 0.0589 Inf
## black MALE middle - other FEMALE middle -0.230618 0.1383 Inf
## black MALE middle - white MALE high 0.069601 0.0809 Inf
## black MALE middle - latinx MALE high -0.276265 0.1929 Inf
## black MALE middle - black MALE high -0.066141 0.0689 Inf
## black MALE middle - other MALE high -0.081772 0.1344 Inf
## black MALE middle - white FEMALE high 0.091334 0.0941 Inf
## black MALE middle - latinx FEMALE high -0.105965 0.2049 Inf
## black MALE middle - black FEMALE high 0.021364 0.0754 Inf
## black MALE middle - other FEMALE high -0.144166 0.1745 Inf
## other MALE middle - white FEMALE middle -0.112276 0.1009 Inf
## other MALE middle - latinx FEMALE middle -0.604343 0.3175 Inf
## other MALE middle - black FEMALE middle -0.065407 0.0921 Inf
## other MALE middle - other FEMALE middle -0.202681 0.1554 Inf
## other MALE middle - white MALE high 0.097538 0.1075 Inf
## other MALE middle - latinx MALE high -0.248328 0.2054 Inf
## other MALE middle - black MALE high -0.038205 0.0987 Inf
## other MALE middle - other MALE high -0.053835 0.1519 Inf
## other MALE middle - white FEMALE high 0.119271 0.1177 Inf
## other MALE middle - latinx FEMALE high -0.078029 0.2168 Inf
## other MALE middle - black FEMALE high 0.049300 0.1034 Inf
## other MALE middle - other FEMALE high -0.116230 0.1883 Inf
## white FEMALE middle - latinx FEMALE middle -0.492067 0.3135 Inf
## white FEMALE middle - black FEMALE middle 0.046868 0.0770 Inf
## white FEMALE middle - other FEMALE middle -0.090405 0.1469 Inf
## white FEMALE middle - white MALE high 0.209814 0.0948 Inf
## white FEMALE middle - latinx MALE high -0.136052 0.1991 Inf
## white FEMALE middle - black MALE high 0.074071 0.0848 Inf
## white FEMALE middle - other MALE high 0.058440 0.1433 Inf
## white FEMALE middle - white FEMALE high 0.231547 0.1063 Inf
## white FEMALE middle - latinx FEMALE high 0.034247 0.2108 Inf
## white FEMALE middle - black FEMALE high 0.161576 0.0902 Inf
## white FEMALE middle - other FEMALE high -0.003954 0.1814 Inf
## latinx FEMALE middle - black FEMALE middle 0.538935 0.3108 Inf
## latinx FEMALE middle - other FEMALE middle 0.401662 0.3350 Inf
## latinx FEMALE middle - white MALE high 0.701881 0.3157 Inf
## latinx FEMALE middle - latinx MALE high 0.356015 0.3610 Inf
## latinx FEMALE middle - black MALE high 0.566138 0.3128 Inf
## latinx FEMALE middle - other MALE high 0.550508 0.3334 Inf
## latinx FEMALE middle - white FEMALE high 0.723614 0.3193 Inf
## latinx FEMALE middle - latinx FEMALE high 0.526314 0.3675 Inf
## latinx FEMALE middle - black FEMALE high 0.653643 0.3143 Inf
## latinx FEMALE middle - other FEMALE high 0.488113 0.3515 Inf
## black FEMALE middle - other FEMALE middle -0.137274 0.1410 Inf
## black FEMALE middle - white MALE high 0.162945 0.0854 Inf
## black FEMALE middle - latinx MALE high -0.182921 0.1948 Inf
## black FEMALE middle - black MALE high 0.027202 0.0741 Inf
## black FEMALE middle - other MALE high 0.011572 0.1372 Inf
## black FEMALE middle - white FEMALE high 0.184678 0.0980 Inf
## black FEMALE middle - latinx FEMALE high -0.012621 0.2067 Inf
## black FEMALE middle - black FEMALE high 0.114707 0.0802 Inf
## black FEMALE middle - other FEMALE high -0.050822 0.1766 Inf
## other FEMALE middle - white MALE high 0.300219 0.1515 Inf
## other FEMALE middle - latinx MALE high -0.045647 0.2315 Inf
## other FEMALE middle - black MALE high 0.164476 0.1455 Inf
## other FEMALE middle - other MALE high 0.148846 0.1857 Inf
## other FEMALE middle - white FEMALE high 0.321952 0.1589 Inf
## other FEMALE middle - latinx FEMALE high 0.124652 0.2417 Inf
## other FEMALE middle - black FEMALE high 0.251981 0.1487 Inf
## other FEMALE middle - other FEMALE high 0.086451 0.2165 Inf
## white MALE high - latinx MALE high -0.345866 0.2025 Inf
## white MALE high - black MALE high -0.135743 0.0925 Inf
## white MALE high - other MALE high -0.151373 0.1480 Inf
## white MALE high - white FEMALE high 0.021733 0.1125 Inf
## white MALE high - latinx FEMALE high -0.175567 0.2140 Inf
## white MALE high - black FEMALE high -0.048238 0.0975 Inf
## white MALE high - other FEMALE high -0.213768 0.1851 Inf
## latinx MALE high - black MALE high 0.210123 0.1980 Inf
## latinx MALE high - other MALE high 0.194493 0.2292 Inf
## latinx MALE high - white FEMALE high 0.367599 0.2081 Inf
## latinx MALE high - latinx FEMALE high 0.170299 0.2765 Inf
## latinx MALE high - black FEMALE high 0.297628 0.2004 Inf
## latinx MALE high - other FEMALE high 0.132098 0.2548 Inf
## black MALE high - other MALE high -0.015630 0.1418 Inf
## black MALE high - white FEMALE high 0.157476 0.1042 Inf
## black MALE high - latinx FEMALE high -0.039824 0.2098 Inf
## black MALE high - black FEMALE high 0.087505 0.0878 Inf
## black MALE high - other FEMALE high -0.078025 0.1802 Inf
## other MALE high - white FEMALE high 0.173106 0.1555 Inf
## other MALE high - latinx FEMALE high -0.024193 0.2394 Inf
## other MALE high - black FEMALE high 0.103135 0.1450 Inf
## other MALE high - other FEMALE high -0.062394 0.2140 Inf
## white FEMALE high - latinx FEMALE high -0.197300 0.2193 Inf
## white FEMALE high - black FEMALE high -0.069971 0.1087 Inf
## white FEMALE high - other FEMALE high -0.235501 0.1912 Inf
## latinx FEMALE high - black FEMALE high 0.127329 0.2120 Inf
## latinx FEMALE high - other FEMALE high -0.038201 0.2640 Inf
## black FEMALE high - other FEMALE high -0.165530 0.1828 Inf
## z.ratio p.value
## -2.694 0.0071
## -2.913 0.0036
## -1.709 0.0874
## -1.929 0.0537
## -1.211 0.2257
## -0.928 0.3533
## -2.725 0.0064
## -1.076 0.2819
## -1.029 0.3033
## -2.391 0.0168
## -1.895 0.0581
## -3.626 0.0003
## -2.518 0.0118
## -3.542 0.0004
## -2.678 0.0074
## -0.935 0.3499
## -2.185 0.0289
## -2.802 0.0051
## -1.682 0.0926
## -0.607 0.5437
## -1.242 0.2141
## -1.565 0.1175
## -1.665 0.0960
## 1.811 0.0702
## 1.801 0.0717
## 1.880 0.0602
## -0.234 0.8148
## 2.348 0.0189
## 0.866 0.3865
## 2.369 0.0178
## 1.743 0.0814
## 2.043 0.0411
## 1.826 0.0678
## 1.377 0.1685
## -0.475 0.6350
## 1.610 0.1074
## 0.869 0.3849
## 2.270 0.0232
## 0.612 0.5408
## 1.710 0.0873
## 1.462 0.1438
## 2.315 0.0206
## 1.167 0.2431
## 2.077 0.0378
## 1.114 0.2652
## 0.217 0.8279
## 0.335 0.7372
## -0.892 0.3725
## 1.705 0.0882
## -1.321 0.1864
## 1.920 0.0548
## 0.326 0.7446
## 0.831 0.4059
## 0.226 0.8215
## -1.195 0.2323
## -1.878 0.0603
## -0.692 0.4891
## -1.292 0.1964
## 1.389 0.1647
## -1.169 0.2423
## -0.237 0.8126
## -0.242 0.8090
## 1.433 0.1519
## -0.277 0.7814
## 0.876 0.3810
## -0.541 0.5883
## 0.057 0.9545
## -0.920 0.3577
## 0.972 0.3313
## -1.288 0.1978
## 1.011 0.3120
## 0.154 0.8773
## 0.270 0.7869
## -0.012 0.9908
## -1.034 0.3012
## -1.890 0.0588
## -0.655 0.5122
## -1.264 0.2061
## 0.829 0.4073
## -1.189 0.2344
## -0.367 0.7138
## -0.349 0.7268
## 0.939 0.3478
## -0.359 0.7195
## 0.427 0.6696
## -0.609 0.5428
## -0.934 0.3504
## 1.053 0.2922
## -1.415 0.1571
## 1.119 0.2632
## 0.121 0.9035
## 0.244 0.8073
## -0.075 0.9406
## -1.261 0.2071
## -1.939 0.0525
## -0.854 0.3931
## -1.388 0.1652
## 0.875 0.3817
## -1.264 0.2061
## -0.497 0.6194
## -0.417 0.6765
## 0.984 0.3250
## -0.402 0.6879
## 0.421 0.6739
## -0.670 0.5026
## 1.093 0.2743
## 0.588 0.5564
## 1.092 0.2749
## 0.947 0.3435
## 0.970 0.3320
## 0.920 0.3577
## 0.744 0.4567
## -0.034 0.9729
## 0.821 0.4119
## 0.590 0.5549
## 1.076 0.2820
## 0.508 0.6115
## 0.863 0.3884
## 0.824 0.4100
## 1.107 0.2682
## 0.764 0.4448
## 1.000 0.3171
## 0.716 0.4742
## -2.159 0.0308
## -0.027 0.9781
## -0.526 0.5986
## -1.129 0.2591
## -1.075 0.2824
## -2.555 0.0106
## -2.272 0.0231
## -2.291 0.0220
## -2.121 0.0339
## -0.102 0.9186
## -1.791 0.0733
## -1.744 0.0812
## -1.130 0.2583
## 0.116 0.9077
## -0.881 0.3784
## -0.651 0.5150
## -1.236 0.2164
## 2.217 0.0266
## 1.207 0.2274
## 1.703 0.0886
## 1.330 0.1836
## 0.634 0.5259
## -1.197 0.2314
## 0.998 0.3181
## 0.009 0.9931
## 2.016 0.0438
## -0.191 0.8485
## 1.157 0.2474
## 0.817 0.4141
## 2.058 0.0396
## 0.525 0.5996
## 1.727 0.0842
## 0.409 0.6824
## -0.528 0.5974
## -1.287 0.1981
## -1.133 0.2573
## -2.797 0.0052
## -2.281 0.0225
## -2.588 0.0096
## -2.176 0.0296
## -0.087 0.9309
## -1.811 0.0701
## -1.912 0.0558
## -1.154 0.2484
## 0.145 0.8849
## -0.884 0.3765
## -0.688 0.4912
## -1.250 0.2111
## 0.006 0.9956
## -0.169 0.8661
## -0.913 0.3614
## -1.870 0.0614
## -0.629 0.5292
## -1.190 0.2340
## 0.448 0.6541
## -1.171 0.2417
## -0.432 0.6659
## -0.425 0.6705
## 0.561 0.5750
## -0.429 0.6680
## 0.144 0.8858
## -0.650 0.5155
## -0.318 0.7507
## -1.949 0.0513
## -2.042 0.0411
## -1.584 0.1133
## -1.667 0.0955
## 0.860 0.3896
## -1.432 0.1520
## -0.960 0.3371
## -0.608 0.5430
## 0.971 0.3315
## -0.517 0.6050
## 0.283 0.7769
## -0.826 0.4086
## -1.113 0.2658
## -1.903 0.0570
## -0.710 0.4775
## -1.304 0.1921
## 0.908 0.3641
## -1.209 0.2267
## -0.387 0.6988
## -0.354 0.7231
## 1.013 0.3108
## -0.360 0.7189
## 0.477 0.6335
## -0.617 0.5370
## -1.570 0.1165
## 0.609 0.5426
## -0.615 0.5384
## 2.212 0.0269
## -0.683 0.4944
## 0.873 0.3826
## 0.408 0.6833
## 2.179 0.0294
## 0.162 0.8709
## 1.791 0.0732
## -0.022 0.9826
## 1.734 0.0829
## 1.199 0.2306
## 2.223 0.0262
## 0.986 0.3240
## 1.810 0.0703
## 1.651 0.0987
## 2.266 0.0234
## 1.432 0.1522
## 2.080 0.0376
## 1.389 0.1649
## -0.973 0.3303
## 1.908 0.0564
## -0.939 0.3477
## 0.367 0.7137
## 0.084 0.9328
## 1.885 0.0594
## -0.061 0.9513
## 1.430 0.1527
## -0.288 0.7735
## 1.981 0.0475
## -0.197 0.8437
## 1.131 0.2582
## 0.802 0.4228
## 2.026 0.0428
## 0.516 0.6060
## 1.695 0.0901
## 0.399 0.6896
## -1.708 0.0877
## -1.467 0.1424
## -1.023 0.3063
## 0.193 0.8469
## -0.820 0.4120
## -0.495 0.6207
## -1.155 0.2481
## 1.061 0.2887
## 0.848 0.3962
## 1.766 0.0774
## 0.616 0.5380
## 1.485 0.1375
## 0.518 0.6041
## -0.110 0.9122
## 1.511 0.1309
## -0.190 0.8494
## 0.997 0.3189
## -0.433 0.6650
## 1.113 0.2657
## -0.101 0.9195
## 0.711 0.4770
## -0.292 0.7706
## -0.900 0.3683
## -0.644 0.5196
## -1.232 0.2181
## 0.601 0.5481
## -0.145 0.8849
## -0.906 0.3651Conscientiousness
cons <- glm(ever_suspended ~ race * sex * consc_tipi * school_group,
family = binomial, data = nlsy_l)
summary(cons)##
## Call:
## glm(formula = ever_suspended ~ race * sex * consc_tipi * school_group,
## family = binomial, data = nlsy_l)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0859 -0.6644 -0.5525 -0.3580 2.6714
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -1.3503339 0.2834099
## racelatinx 1.2698957 1.2688052
## raceblack 1.1223766 0.3984650
## raceother -1.1393990 0.6277496
## sexFEMALE -1.3792334 0.5250671
## consc_tipi -0.0753678 0.0529055
## school_groupmiddle -0.1158010 0.3949413
## school_grouphigh 0.1944707 0.5003260
## racelatinx:sexFEMALE -7.4275510 6.6424954
## raceblack:sexFEMALE 0.1019951 0.7169407
## raceother:sexFEMALE 2.7481652 1.0277494
## racelatinx:consc_tipi -0.1188718 0.2232485
## raceblack:consc_tipi 0.0766237 0.0721710
## raceother:consc_tipi 0.3357126 0.1114286
## sexFEMALE:consc_tipi 0.0782008 0.0955262
## racelatinx:school_groupmiddle 0.2322850 1.4635172
## raceblack:school_groupmiddle -0.4808603 0.5401470
## raceother:school_groupmiddle 1.0727881 0.8453719
## racelatinx:school_grouphigh -0.3019598 1.8148751
## raceblack:school_grouphigh -0.7314502 0.6871803
## raceother:school_grouphigh 0.9952537 1.1041480
## sexFEMALE:school_groupmiddle -0.0370804 0.7165199
## sexFEMALE:school_grouphigh 0.3491075 0.8801496
## consc_tipi:school_groupmiddle 0.0732415 0.0726912
## consc_tipi:school_grouphigh 0.0677105 0.0915619
## racelatinx:sexFEMALE:consc_tipi 1.0073326 1.0249501
## raceblack:sexFEMALE:consc_tipi -0.0600404 0.1268350
## raceother:sexFEMALE:consc_tipi -0.5759790 0.1877657
## racelatinx:sexFEMALE:school_groupmiddle -2.2582901 8.0712560
## raceblack:sexFEMALE:school_groupmiddle 0.5544712 0.9526610
## raceother:sexFEMALE:school_groupmiddle -1.9692239 1.4571609
## racelatinx:sexFEMALE:school_grouphigh 8.0833745 6.8674462
## raceblack:sexFEMALE:school_grouphigh 0.2427842 1.1611478
## raceother:sexFEMALE:school_grouphigh -0.3253011 1.7106570
## racelatinx:consc_tipi:school_groupmiddle -0.1034926 0.2596684
## raceblack:consc_tipi:school_groupmiddle -0.0271192 0.0970164
## raceother:consc_tipi:school_groupmiddle -0.2334434 0.1502384
## racelatinx:consc_tipi:school_grouphigh 0.0174504 0.3140200
## raceblack:consc_tipi:school_grouphigh 0.0009426 0.1224231
## raceother:consc_tipi:school_grouphigh -0.2469573 0.1942345
## sexFEMALE:consc_tipi:school_groupmiddle 0.0346310 0.1285437
## sexFEMALE:consc_tipi:school_grouphigh -0.0449485 0.1574677
## racelatinx:sexFEMALE:consc_tipi:school_groupmiddle 0.4377364 1.2396422
## raceblack:sexFEMALE:consc_tipi:school_groupmiddle -0.0815089 0.1674660
## raceother:sexFEMALE:consc_tipi:school_groupmiddle 0.3311126 0.2626064
## racelatinx:sexFEMALE:consc_tipi:school_grouphigh -1.1058475 1.0693497
## raceblack:sexFEMALE:consc_tipi:school_grouphigh 0.0062328 0.2033424
## raceother:sexFEMALE:consc_tipi:school_grouphigh 0.0688884 0.3134655
## z value Pr(>|z|)
## (Intercept) -4.765 1.89e-06 ***
## racelatinx 1.001 0.31689
## raceblack 2.817 0.00485 **
## raceother -1.815 0.06952 .
## sexFEMALE -2.627 0.00862 **
## consc_tipi -1.425 0.15428
## school_groupmiddle -0.293 0.76936
## school_grouphigh 0.389 0.69751
## racelatinx:sexFEMALE -1.118 0.26349
## raceblack:sexFEMALE 0.142 0.88687
## raceother:sexFEMALE 2.674 0.00750 **
## racelatinx:consc_tipi -0.532 0.59440
## raceblack:consc_tipi 1.062 0.28837
## raceother:consc_tipi 3.013 0.00259 **
## sexFEMALE:consc_tipi 0.819 0.41300
## racelatinx:school_groupmiddle 0.159 0.87389
## raceblack:school_groupmiddle -0.890 0.37334
## raceother:school_groupmiddle 1.269 0.20444
## racelatinx:school_grouphigh -0.166 0.86786
## raceblack:school_grouphigh -1.064 0.28714
## raceother:school_grouphigh 0.901 0.36739
## sexFEMALE:school_groupmiddle -0.052 0.95873
## sexFEMALE:school_grouphigh 0.397 0.69163
## consc_tipi:school_groupmiddle 1.008 0.31366
## consc_tipi:school_grouphigh 0.740 0.45960
## racelatinx:sexFEMALE:consc_tipi 0.983 0.32570
## raceblack:sexFEMALE:consc_tipi -0.473 0.63595
## raceother:sexFEMALE:consc_tipi -3.068 0.00216 **
## racelatinx:sexFEMALE:school_groupmiddle -0.280 0.77964
## raceblack:sexFEMALE:school_groupmiddle 0.582 0.56055
## raceother:sexFEMALE:school_groupmiddle -1.351 0.17656
## racelatinx:sexFEMALE:school_grouphigh 1.177 0.23917
## raceblack:sexFEMALE:school_grouphigh 0.209 0.83438
## raceother:sexFEMALE:school_grouphigh -0.190 0.84918
## racelatinx:consc_tipi:school_groupmiddle -0.399 0.69022
## raceblack:consc_tipi:school_groupmiddle -0.280 0.77984
## raceother:consc_tipi:school_groupmiddle -1.554 0.12023
## racelatinx:consc_tipi:school_grouphigh 0.056 0.95568
## raceblack:consc_tipi:school_grouphigh 0.008 0.99386
## raceother:consc_tipi:school_grouphigh -1.271 0.20357
## sexFEMALE:consc_tipi:school_groupmiddle 0.269 0.78761
## sexFEMALE:consc_tipi:school_grouphigh -0.285 0.77530
## racelatinx:sexFEMALE:consc_tipi:school_groupmiddle 0.353 0.72400
## raceblack:sexFEMALE:consc_tipi:school_groupmiddle -0.487 0.62646
## raceother:sexFEMALE:consc_tipi:school_groupmiddle 1.261 0.20736
## racelatinx:sexFEMALE:consc_tipi:school_grouphigh -1.034 0.30108
## raceblack:sexFEMALE:consc_tipi:school_grouphigh 0.031 0.97555
## raceother:sexFEMALE:consc_tipi:school_grouphigh 0.220 0.82605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 18409 on 18336 degrees of freedom
## Residual deviance: 16959 on 18289 degrees of freedom
## AIC: 17055
##
## Number of Fisher Scoring iterations: 6plot_model(cons, type = "pred", terms = c("consc_tipi", "race", "sex", "school_group"))
object1 <- emtrends(cons, pairwise ~ race * sex * school_group, var = "consc_tipi", adjust = "none")
object1## $emtrends
## race sex school_group consc_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE elementary -0.07537 0.0529 Inf -0.17906 0.0283
## latinx MALE elementary -0.19424 0.2169 Inf -0.61933 0.2309
## black MALE elementary 0.00126 0.0491 Inf -0.09496 0.0975
## other MALE elementary 0.26034 0.0981 Inf 0.06813 0.4526
## white FEMALE elementary 0.00283 0.0795 Inf -0.15306 0.1587
## latinx FEMALE elementary 0.89129 0.9972 Inf -1.06313 2.8457
## black FEMALE elementary 0.01942 0.0675 Inf -0.11282 0.1517
## other FEMALE elementary -0.23743 0.1285 Inf -0.48930 0.0144
## white MALE middle -0.00213 0.0498 Inf -0.09983 0.0956
## latinx MALE middle -0.22449 0.1229 Inf -0.46536 0.0164
## black MALE middle 0.04738 0.0415 Inf -0.03387 0.1286
## other MALE middle 0.10014 0.0876 Inf -0.07151 0.2718
## white FEMALE middle 0.11071 0.0701 Inf -0.02668 0.2481
## latinx FEMALE middle 1.33341 0.6809 Inf -0.00122 2.6680
## black FEMALE middle 0.01866 0.0533 Inf -0.08581 0.1231
## other FEMALE middle -0.03189 0.1365 Inf -0.29947 0.2357
## white MALE high -0.00766 0.0747 Inf -0.15413 0.1388
## latinx MALE high -0.10908 0.2078 Inf -0.51637 0.2982
## black MALE high 0.06991 0.0648 Inf -0.05702 0.1968
## other MALE high 0.08110 0.1404 Inf -0.19418 0.3564
## white FEMALE high 0.02559 0.1004 Inf -0.17124 0.2224
## latinx FEMALE high -0.17434 0.1847 Inf -0.53644 0.1878
## black FEMALE high 0.04935 0.0735 Inf -0.09462 0.1933
## other FEMALE high -0.39274 0.1662 Inf -0.71840 -0.0671
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df
## white MALE elementary - latinx MALE elementary 0.118872 0.2232 Inf
## white MALE elementary - black MALE elementary -0.076624 0.0722 Inf
## white MALE elementary - other MALE elementary -0.335713 0.1114 Inf
## white MALE elementary - white FEMALE elementary -0.078201 0.0955 Inf
## white MALE elementary - latinx FEMALE elementary -0.966662 0.9986 Inf
## white MALE elementary - black FEMALE elementary -0.094784 0.0857 Inf
## white MALE elementary - other FEMALE elementary 0.162066 0.1390 Inf
## white MALE elementary - white MALE middle -0.073242 0.0727 Inf
## white MALE elementary - latinx MALE middle 0.149123 0.1338 Inf
## white MALE elementary - black MALE middle -0.122746 0.0672 Inf
## white MALE elementary - other MALE middle -0.175511 0.1023 Inf
## white MALE elementary - white FEMALE middle -0.186073 0.0878 Inf
## white MALE elementary - latinx FEMALE middle -1.408778 0.6830 Inf
## white MALE elementary - black FEMALE middle -0.094029 0.0751 Inf
## white MALE elementary - other FEMALE middle -0.043476 0.1464 Inf
## white MALE elementary - white MALE high -0.067711 0.0916 Inf
## white MALE elementary - latinx MALE high 0.033711 0.2144 Inf
## white MALE elementary - black MALE high -0.145277 0.0836 Inf
## white MALE elementary - other MALE high -0.156466 0.1501 Inf
## white MALE elementary - white FEMALE high -0.100963 0.1135 Inf
## white MALE elementary - latinx FEMALE high 0.098974 0.1922 Inf
## white MALE elementary - black FEMALE high -0.124721 0.0905 Inf
## white MALE elementary - other FEMALE high 0.317373 0.1744 Inf
## latinx MALE elementary - black MALE elementary -0.195496 0.2224 Inf
## latinx MALE elementary - other MALE elementary -0.454584 0.2380 Inf
## latinx MALE elementary - white FEMALE elementary -0.197073 0.2310 Inf
## latinx MALE elementary - latinx FEMALE elementary -1.085533 1.0205 Inf
## latinx MALE elementary - black FEMALE elementary -0.213656 0.2271 Inf
## latinx MALE elementary - other FEMALE elementary 0.043194 0.2521 Inf
## latinx MALE elementary - white MALE middle -0.192113 0.2225 Inf
## latinx MALE elementary - latinx MALE middle 0.030251 0.2493 Inf
## latinx MALE elementary - black MALE middle -0.241618 0.2208 Inf
## latinx MALE elementary - other MALE middle -0.294382 0.2339 Inf
## latinx MALE elementary - white FEMALE middle -0.304945 0.2279 Inf
## latinx MALE elementary - latinx FEMALE middle -1.527650 0.7147 Inf
## latinx MALE elementary - black FEMALE middle -0.212900 0.2233 Inf
## latinx MALE elementary - other FEMALE middle -0.162348 0.2563 Inf
## latinx MALE elementary - white MALE high -0.186582 0.2294 Inf
## latinx MALE elementary - latinx MALE high -0.085161 0.3004 Inf
## latinx MALE elementary - black MALE high -0.264149 0.2264 Inf
## latinx MALE elementary - other MALE high -0.275338 0.2584 Inf
## latinx MALE elementary - white FEMALE high -0.219835 0.2390 Inf
## latinx MALE elementary - latinx FEMALE high -0.019898 0.2849 Inf
## latinx MALE elementary - black FEMALE high -0.243593 0.2290 Inf
## latinx MALE elementary - other FEMALE high 0.198501 0.2732 Inf
## black MALE elementary - other MALE elementary -0.259089 0.1097 Inf
## black MALE elementary - white FEMALE elementary -0.001577 0.0935 Inf
## black MALE elementary - latinx FEMALE elementary -0.890038 0.9984 Inf
## black MALE elementary - black FEMALE elementary -0.018160 0.0834 Inf
## black MALE elementary - other FEMALE elementary 0.238689 0.1376 Inf
## black MALE elementary - white MALE middle 0.003382 0.0700 Inf
## black MALE elementary - latinx MALE middle 0.225747 0.1323 Inf
## black MALE elementary - black MALE middle -0.046122 0.0643 Inf
## black MALE elementary - other MALE middle -0.098887 0.1004 Inf
## black MALE elementary - white FEMALE middle -0.109450 0.0856 Inf
## black MALE elementary - latinx FEMALE middle -1.332154 0.6827 Inf
## black MALE elementary - black FEMALE middle -0.017405 0.0725 Inf
## black MALE elementary - other FEMALE middle 0.033148 0.1451 Inf
## black MALE elementary - white MALE high 0.008913 0.0894 Inf
## black MALE elementary - latinx MALE high 0.110335 0.2135 Inf
## black MALE elementary - black MALE high -0.068653 0.0813 Inf
## black MALE elementary - other MALE high -0.079842 0.1488 Inf
## black MALE elementary - white FEMALE high -0.024339 0.1118 Inf
## black MALE elementary - latinx FEMALE high 0.175597 0.1912 Inf
## black MALE elementary - black FEMALE high -0.048098 0.0883 Inf
## black MALE elementary - other FEMALE high 0.393996 0.1733 Inf
## other MALE elementary - white FEMALE elementary 0.257512 0.1263 Inf
## other MALE elementary - latinx FEMALE elementary -0.630949 1.0020 Inf
## other MALE elementary - black FEMALE elementary 0.240928 0.1190 Inf
## other MALE elementary - other FEMALE elementary 0.497778 0.1616 Inf
## other MALE elementary - white MALE middle 0.262471 0.1100 Inf
## other MALE elementary - latinx MALE middle 0.484835 0.1572 Inf
## other MALE elementary - black MALE middle 0.212967 0.1065 Inf
## other MALE elementary - other MALE middle 0.160202 0.1315 Inf
## other MALE elementary - white FEMALE middle 0.149639 0.1205 Inf
## other MALE elementary - latinx FEMALE middle -1.073066 0.6880 Inf
## other MALE elementary - black FEMALE middle 0.241684 0.1116 Inf
## other MALE elementary - other FEMALE middle 0.292236 0.1681 Inf
## other MALE elementary - white MALE high 0.268002 0.1233 Inf
## other MALE elementary - latinx MALE high 0.369424 0.2298 Inf
## other MALE elementary - black MALE high 0.190436 0.1175 Inf
## other MALE elementary - other MALE high 0.179247 0.1713 Inf
## other MALE elementary - white FEMALE high 0.234750 0.1404 Inf
## other MALE elementary - latinx FEMALE high 0.434686 0.2092 Inf
## other MALE elementary - black FEMALE high 0.210991 0.1225 Inf
## other MALE elementary - other FEMALE high 0.653085 0.1929 Inf
## white FEMALE elementary - latinx FEMALE elementary -0.888461 1.0003 Inf
## white FEMALE elementary - black FEMALE elementary -0.016583 0.1043 Inf
## white FEMALE elementary - other FEMALE elementary 0.240266 0.1511 Inf
## white FEMALE elementary - white MALE middle 0.004959 0.0939 Inf
## white FEMALE elementary - latinx MALE middle 0.227324 0.1464 Inf
## white FEMALE elementary - black MALE middle -0.044545 0.0897 Inf
## white FEMALE elementary - other MALE middle -0.097310 0.1183 Inf
## white FEMALE elementary - white FEMALE middle -0.107873 0.1060 Inf
## white FEMALE elementary - latinx FEMALE middle -1.330577 0.6856 Inf
## white FEMALE elementary - black FEMALE middle -0.015828 0.0957 Inf
## white FEMALE elementary - other FEMALE middle 0.034725 0.1580 Inf
## white FEMALE elementary - white MALE high 0.010490 0.1091 Inf
## white FEMALE elementary - latinx MALE high 0.111912 0.2225 Inf
## white FEMALE elementary - black MALE high -0.067076 0.1026 Inf
## white FEMALE elementary - other MALE high -0.078265 0.1614 Inf
## white FEMALE elementary - white FEMALE high -0.022762 0.1281 Inf
## white FEMALE elementary - latinx FEMALE high 0.177174 0.2011 Inf
## white FEMALE elementary - black FEMALE high -0.046521 0.1083 Inf
## white FEMALE elementary - other FEMALE high 0.395573 0.1842 Inf
## latinx FEMALE elementary - black FEMALE elementary 0.871877 0.9995 Inf
## latinx FEMALE elementary - other FEMALE elementary 1.128727 1.0054 Inf
## latinx FEMALE elementary - white MALE middle 0.893420 0.9984 Inf
## latinx FEMALE elementary - latinx MALE middle 1.115784 1.0047 Inf
## latinx FEMALE elementary - black MALE middle 0.843916 0.9980 Inf
## latinx FEMALE elementary - other MALE middle 0.791151 1.0010 Inf
## latinx FEMALE elementary - white FEMALE middle 0.780588 0.9996 Inf
## latinx FEMALE elementary - latinx FEMALE middle -0.442116 1.2075 Inf
## latinx FEMALE elementary - black FEMALE middle 0.872633 0.9986 Inf
## latinx FEMALE elementary - other FEMALE middle 0.923185 1.0065 Inf
## latinx FEMALE elementary - white MALE high 0.898951 1.0000 Inf
## latinx FEMALE elementary - latinx MALE high 1.000373 1.0186 Inf
## latinx FEMALE elementary - black MALE high 0.821385 0.9993 Inf
## latinx FEMALE elementary - other MALE high 0.810196 1.0070 Inf
## latinx FEMALE elementary - white FEMALE high 0.865699 1.0022 Inf
## latinx FEMALE elementary - latinx FEMALE high 1.065635 1.0141 Inf
## latinx FEMALE elementary - black FEMALE high 0.841940 0.9999 Inf
## latinx FEMALE elementary - other FEMALE high 1.284034 1.0109 Inf
## black FEMALE elementary - other FEMALE elementary 0.256850 0.1451 Inf
## black FEMALE elementary - white MALE middle 0.021543 0.0839 Inf
## black FEMALE elementary - latinx MALE middle 0.243907 0.1402 Inf
## black FEMALE elementary - black MALE middle -0.027962 0.0792 Inf
## black FEMALE elementary - other MALE middle -0.080727 0.1106 Inf
## black FEMALE elementary - white FEMALE middle -0.091289 0.0973 Inf
## black FEMALE elementary - latinx FEMALE middle -1.313994 0.6843 Inf
## black FEMALE elementary - black FEMALE middle 0.000755 0.0860 Inf
## black FEMALE elementary - other FEMALE middle 0.051308 0.1523 Inf
## black FEMALE elementary - white MALE high 0.027074 0.1007 Inf
## black FEMALE elementary - latinx MALE high 0.128495 0.2185 Inf
## black FEMALE elementary - black MALE high -0.050493 0.0935 Inf
## black FEMALE elementary - other MALE high -0.061682 0.1558 Inf
## black FEMALE elementary - white FEMALE high -0.006179 0.1210 Inf
## black FEMALE elementary - latinx FEMALE high 0.193758 0.1967 Inf
## black FEMALE elementary - black FEMALE high -0.029937 0.0997 Inf
## black FEMALE elementary - other FEMALE high 0.412157 0.1793 Inf
## other FEMALE elementary - white MALE middle -0.235307 0.1378 Inf
## other FEMALE elementary - latinx MALE middle -0.012943 0.1778 Inf
## other FEMALE elementary - black MALE middle -0.284812 0.1350 Inf
## other FEMALE elementary - other MALE middle -0.337576 0.1555 Inf
## other FEMALE elementary - white FEMALE middle -0.348139 0.1464 Inf
## other FEMALE elementary - latinx FEMALE middle -1.570844 0.6930 Inf
## other FEMALE elementary - black FEMALE middle -0.256094 0.1391 Inf
## other FEMALE elementary - other FEMALE middle -0.205542 0.1875 Inf
## other FEMALE elementary - white MALE high -0.229776 0.1487 Inf
## other FEMALE elementary - latinx MALE high -0.128355 0.2443 Inf
## other FEMALE elementary - black MALE high -0.307342 0.1439 Inf
## other FEMALE elementary - other MALE high -0.318531 0.1904 Inf
## other FEMALE elementary - white FEMALE high -0.263028 0.1631 Inf
## other FEMALE elementary - latinx FEMALE high -0.063092 0.2250 Inf
## other FEMALE elementary - black FEMALE high -0.286787 0.1480 Inf
## other FEMALE elementary - other FEMALE high 0.155307 0.2101 Inf
## white MALE middle - latinx MALE middle 0.222364 0.1326 Inf
## white MALE middle - black MALE middle -0.049504 0.0648 Inf
## white MALE middle - other MALE middle -0.102269 0.1008 Inf
## white MALE middle - white FEMALE middle -0.112832 0.0860 Inf
## white MALE middle - latinx FEMALE middle -1.335536 0.6828 Inf
## white MALE middle - black FEMALE middle -0.020787 0.0730 Inf
## white MALE middle - other FEMALE middle 0.029765 0.1453 Inf
## white MALE middle - white MALE high 0.005531 0.0898 Inf
## white MALE middle - latinx MALE high 0.106953 0.2137 Inf
## white MALE middle - black MALE high -0.072035 0.0817 Inf
## white MALE middle - other MALE high -0.083224 0.1490 Inf
## white MALE middle - white FEMALE high -0.027721 0.1121 Inf
## white MALE middle - latinx FEMALE high 0.172215 0.1914 Inf
## white MALE middle - black FEMALE high -0.051480 0.0888 Inf
## white MALE middle - other FEMALE high 0.390614 0.1735 Inf
## latinx MALE middle - black MALE middle -0.271869 0.1297 Inf
## latinx MALE middle - other MALE middle -0.324634 0.1509 Inf
## latinx MALE middle - white FEMALE middle -0.335196 0.1415 Inf
## latinx MALE middle - latinx FEMALE middle -1.557901 0.6919 Inf
## latinx MALE middle - black FEMALE middle -0.243151 0.1340 Inf
## latinx MALE middle - other FEMALE middle -0.192599 0.1837 Inf
## latinx MALE middle - white MALE high -0.216833 0.1438 Inf
## latinx MALE middle - latinx MALE high -0.115412 0.2414 Inf
## latinx MALE middle - black MALE high -0.294400 0.1389 Inf
## latinx MALE middle - other MALE high -0.305589 0.1866 Inf
## latinx MALE middle - white FEMALE high -0.250086 0.1587 Inf
## latinx MALE middle - latinx FEMALE high -0.050149 0.2219 Inf
## latinx MALE middle - black FEMALE high -0.273844 0.1432 Inf
## latinx MALE middle - other FEMALE high 0.168250 0.2067 Inf
## black MALE middle - other MALE middle -0.052765 0.0969 Inf
## black MALE middle - white FEMALE middle -0.063327 0.0814 Inf
## black MALE middle - latinx FEMALE middle -1.286032 0.6822 Inf
## black MALE middle - black FEMALE middle 0.028717 0.0675 Inf
## black MALE middle - other FEMALE middle 0.079270 0.1427 Inf
## black MALE middle - white MALE high 0.055036 0.0855 Inf
## black MALE middle - latinx MALE high 0.156457 0.2119 Inf
## black MALE middle - black MALE high -0.022531 0.0769 Inf
## black MALE middle - other MALE high -0.033720 0.1464 Inf
## black MALE middle - white FEMALE high 0.021783 0.1086 Inf
## black MALE middle - latinx FEMALE high 0.221720 0.1893 Inf
## black MALE middle - black FEMALE high -0.001975 0.0843 Inf
## black MALE middle - other FEMALE high 0.440119 0.1712 Inf
## other MALE middle - white FEMALE middle -0.010563 0.1122 Inf
## other MALE middle - latinx FEMALE middle -1.233267 0.6866 Inf
## other MALE middle - black FEMALE middle 0.081482 0.1025 Inf
## other MALE middle - other FEMALE middle 0.132034 0.1622 Inf
## other MALE middle - white MALE high 0.107800 0.1151 Inf
## other MALE middle - latinx MALE high 0.209222 0.2255 Inf
## other MALE middle - black MALE high 0.030234 0.1089 Inf
## other MALE middle - other MALE high 0.019045 0.1655 Inf
## other MALE middle - white FEMALE high 0.074548 0.1333 Inf
## other MALE middle - latinx FEMALE high 0.274484 0.2045 Inf
## other MALE middle - black FEMALE high 0.050789 0.1143 Inf
## other MALE middle - other FEMALE high 0.492883 0.1878 Inf
## white FEMALE middle - latinx FEMALE middle -1.222705 0.6845 Inf
## white FEMALE middle - black FEMALE middle 0.092045 0.0881 Inf
## white FEMALE middle - other FEMALE middle 0.142597 0.1535 Inf
## white FEMALE middle - white MALE high 0.118363 0.1025 Inf
## white FEMALE middle - latinx MALE high 0.219784 0.2193 Inf
## white FEMALE middle - black MALE high 0.040797 0.0954 Inf
## white FEMALE middle - other MALE high 0.029608 0.1570 Inf
## white FEMALE middle - white FEMALE high 0.085111 0.1225 Inf
## white FEMALE middle - latinx FEMALE high 0.285047 0.1976 Inf
## white FEMALE middle - black FEMALE high 0.061352 0.1015 Inf
## white FEMALE middle - other FEMALE high 0.503446 0.1803 Inf
## latinx FEMALE middle - black FEMALE middle 1.314749 0.6830 Inf
## latinx FEMALE middle - other FEMALE middle 1.365302 0.6945 Inf
## latinx FEMALE middle - white MALE high 1.341068 0.6850 Inf
## latinx FEMALE middle - latinx MALE high 1.442489 0.7120 Inf
## latinx FEMALE middle - black MALE high 1.263501 0.6840 Inf
## latinx FEMALE middle - other MALE high 1.252312 0.6953 Inf
## latinx FEMALE middle - white FEMALE high 1.307815 0.6883 Inf
## latinx FEMALE middle - latinx FEMALE high 1.507751 0.7056 Inf
## latinx FEMALE middle - black FEMALE high 1.284057 0.6849 Inf
## latinx FEMALE middle - other FEMALE high 1.726150 0.7009 Inf
## black FEMALE middle - other FEMALE middle 0.050552 0.1466 Inf
## black FEMALE middle - white MALE high 0.026318 0.0918 Inf
## black FEMALE middle - latinx MALE high 0.127740 0.2145 Inf
## black FEMALE middle - black MALE high -0.051248 0.0839 Inf
## black FEMALE middle - other MALE high -0.062437 0.1502 Inf
## black FEMALE middle - white FEMALE high -0.006934 0.1137 Inf
## black FEMALE middle - latinx FEMALE high 0.193002 0.1923 Inf
## black FEMALE middle - black FEMALE high -0.030693 0.0908 Inf
## black FEMALE middle - other FEMALE high 0.411401 0.1745 Inf
## other FEMALE middle - white MALE high -0.024234 0.1556 Inf
## other FEMALE middle - latinx MALE high 0.077187 0.2486 Inf
## other FEMALE middle - black MALE high -0.101801 0.1511 Inf
## other FEMALE middle - other MALE high -0.112990 0.1959 Inf
## other FEMALE middle - white FEMALE high -0.057487 0.1695 Inf
## other FEMALE middle - latinx FEMALE high 0.142450 0.2297 Inf
## other FEMALE middle - black FEMALE high -0.081245 0.1550 Inf
## other FEMALE middle - other FEMALE high 0.360849 0.2150 Inf
## white MALE high - latinx MALE high 0.101421 0.2208 Inf
## white MALE high - black MALE high -0.077566 0.0989 Inf
## white MALE high - other MALE high -0.088755 0.1591 Inf
## white MALE high - white FEMALE high -0.033252 0.1252 Inf
## white MALE high - latinx FEMALE high 0.166684 0.1993 Inf
## white MALE high - black FEMALE high -0.057011 0.1048 Inf
## white MALE high - other FEMALE high 0.385083 0.1822 Inf
## latinx MALE high - black MALE high -0.178988 0.2177 Inf
## latinx MALE high - other MALE high -0.190177 0.2508 Inf
## latinx MALE high - white FEMALE high -0.134674 0.2308 Inf
## latinx MALE high - latinx FEMALE high 0.065263 0.2781 Inf
## latinx MALE high - black FEMALE high -0.158432 0.2204 Inf
## latinx MALE high - other FEMALE high 0.283662 0.2661 Inf
## black MALE high - other MALE high -0.011189 0.1547 Inf
## black MALE high - white FEMALE high 0.044314 0.1195 Inf
## black MALE high - latinx FEMALE high 0.244250 0.1958 Inf
## black MALE high - black FEMALE high 0.020555 0.0979 Inf
## black MALE high - other FEMALE high 0.462649 0.1783 Inf
## other MALE high - white FEMALE high 0.055503 0.1727 Inf
## other MALE high - latinx FEMALE high 0.255439 0.2321 Inf
## other MALE high - black FEMALE high 0.031744 0.1585 Inf
## other MALE high - other FEMALE high 0.473838 0.2176 Inf
## white FEMALE high - latinx FEMALE high 0.199936 0.2103 Inf
## white FEMALE high - black FEMALE high -0.023759 0.1244 Inf
## white FEMALE high - other FEMALE high 0.418335 0.1941 Inf
## latinx FEMALE high - black FEMALE high -0.223695 0.1988 Inf
## latinx FEMALE high - other FEMALE high 0.218399 0.2485 Inf
## black FEMALE high - other FEMALE high 0.442094 0.1817 Inf
## z.ratio p.value
## 0.532 0.5944
## -1.062 0.2884
## -3.013 0.0026
## -0.819 0.4130
## -0.968 0.3330
## -1.105 0.2689
## 1.166 0.2435
## -1.008 0.3137
## 1.115 0.2650
## -1.826 0.0678
## -1.715 0.0863
## -2.119 0.0341
## -2.063 0.0391
## -1.252 0.2106
## -0.297 0.7665
## -0.740 0.4596
## 0.157 0.8751
## -1.737 0.0823
## -1.043 0.2972
## -0.889 0.3738
## 0.515 0.6065
## -1.378 0.1683
## 1.820 0.0688
## -0.879 0.3793
## -1.910 0.0562
## -0.853 0.3936
## -1.064 0.2874
## -0.941 0.3469
## 0.171 0.8640
## -0.863 0.3880
## 0.121 0.9034
## -1.094 0.2739
## -1.259 0.2082
## -1.338 0.1809
## -2.138 0.0325
## -0.953 0.3405
## -0.633 0.5264
## -0.813 0.4160
## -0.284 0.7768
## -1.167 0.2432
## -1.066 0.2866
## -0.920 0.3577
## -0.070 0.9443
## -1.064 0.2874
## 0.727 0.4675
## -2.362 0.0182
## -0.017 0.9865
## -0.891 0.3727
## -0.218 0.8277
## 1.735 0.0827
## 0.048 0.9614
## 1.706 0.0880
## -0.718 0.4729
## -0.985 0.3247
## -1.279 0.2009
## -1.951 0.0510
## -0.240 0.8102
## 0.228 0.8193
## 0.100 0.9206
## 0.517 0.6053
## -0.845 0.3982
## -0.537 0.5915
## -0.218 0.8276
## 0.919 0.3583
## -0.544 0.5862
## 2.274 0.0230
## 2.039 0.0414
## -0.630 0.5289
## 2.024 0.0430
## 3.079 0.0021
## 2.386 0.0170
## 3.084 0.0020
## 2.000 0.0455
## 1.218 0.2231
## 1.241 0.2145
## -1.560 0.1188
## 2.165 0.0304
## 1.739 0.0821
## 2.174 0.0297
## 1.608 0.1079
## 1.620 0.1051
## 1.046 0.2954
## 1.672 0.0945
## 2.078 0.0377
## 1.722 0.0851
## 3.385 0.0007
## -0.888 0.3745
## -0.159 0.8737
## 1.590 0.1119
## 0.053 0.9579
## 1.553 0.1204
## -0.497 0.6194
## -0.823 0.4108
## -1.018 0.3089
## -1.941 0.0523
## -0.165 0.8687
## 0.220 0.8260
## 0.096 0.9234
## 0.503 0.6150
## -0.654 0.5131
## -0.485 0.6278
## -0.178 0.8590
## 0.881 0.3784
## -0.430 0.6674
## 2.147 0.0318
## 0.872 0.3830
## 1.123 0.2616
## 0.895 0.3709
## 1.111 0.2668
## 0.846 0.3978
## 0.790 0.4293
## 0.781 0.4349
## -0.366 0.7143
## 0.874 0.3822
## 0.917 0.3590
## 0.899 0.3687
## 0.982 0.3260
## 0.822 0.4111
## 0.805 0.4211
## 0.864 0.3877
## 1.051 0.2934
## 0.842 0.3998
## 1.270 0.2040
## 1.770 0.0768
## 0.257 0.7973
## 1.740 0.0819
## -0.353 0.7240
## -0.730 0.4653
## -0.938 0.3481
## -1.920 0.0548
## 0.009 0.9930
## 0.337 0.7362
## 0.269 0.7880
## 0.588 0.5565
## -0.540 0.5893
## -0.396 0.6922
## -0.051 0.9593
## 0.985 0.3246
## -0.300 0.7641
## 2.298 0.0215
## -1.707 0.0878
## -0.073 0.9420
## -2.109 0.0349
## -2.171 0.0299
## -2.378 0.0174
## -2.267 0.0234
## -1.841 0.0656
## -1.096 0.2729
## -1.546 0.1222
## -0.525 0.5994
## -2.136 0.0327
## -1.673 0.0943
## -1.613 0.1068
## -0.280 0.7792
## -1.938 0.0527
## 0.739 0.4597
## 1.677 0.0936
## -0.764 0.4451
## -1.015 0.3102
## -1.312 0.1896
## -1.956 0.0505
## -0.285 0.7758
## 0.205 0.8377
## 0.062 0.9509
## 0.500 0.6167
## -0.881 0.3781
## -0.558 0.5766
## -0.247 0.8047
## 0.900 0.3681
## -0.580 0.5620
## 2.252 0.0243
## -2.096 0.0361
## -2.151 0.0315
## -2.369 0.0178
## -2.251 0.0244
## -1.815 0.0695
## -1.049 0.2944
## -1.508 0.1317
## -0.478 0.6326
## -2.119 0.0341
## -1.637 0.1015
## -1.576 0.1151
## -0.226 0.8212
## -1.913 0.0558
## 0.814 0.4156
## -0.545 0.5861
## -0.778 0.4368
## -1.885 0.0594
## 0.425 0.6706
## 0.556 0.5785
## 0.644 0.5196
## 0.738 0.4603
## -0.293 0.7695
## -0.230 0.8179
## 0.200 0.8411
## 1.171 0.2416
## -0.023 0.9813
## 2.570 0.0102
## -0.094 0.9250
## -1.796 0.0724
## 0.795 0.4268
## 0.814 0.4156
## 0.936 0.3491
## 0.928 0.3535
## 0.278 0.7813
## 0.115 0.9084
## 0.559 0.5759
## 1.343 0.1794
## 0.444 0.6568
## 2.624 0.0087
## -1.786 0.0741
## 1.045 0.2959
## 0.929 0.3528
## 1.155 0.2480
## 1.002 0.3163
## 0.427 0.6690
## 0.189 0.8504
## 0.695 0.4871
## 1.443 0.1491
## 0.604 0.5457
## 2.792 0.0052
## 1.925 0.0542
## 1.966 0.0493
## 1.958 0.0503
## 2.026 0.0428
## 1.847 0.0647
## 1.801 0.0717
## 1.900 0.0574
## 2.137 0.0326
## 1.875 0.0608
## 2.463 0.0138
## 0.345 0.7301
## 0.287 0.7743
## 0.595 0.5516
## -0.611 0.5412
## -0.416 0.6777
## -0.061 0.9514
## 1.004 0.3155
## -0.338 0.7352
## 2.358 0.0184
## -0.156 0.8763
## 0.310 0.7562
## -0.674 0.5005
## -0.577 0.5640
## -0.339 0.7345
## 0.620 0.5352
## -0.524 0.6002
## 1.678 0.0933
## 0.459 0.6460
## -0.784 0.4328
## -0.558 0.5769
## -0.266 0.7905
## 0.836 0.4029
## -0.544 0.5864
## 2.114 0.0345
## -0.822 0.4109
## -0.758 0.4483
## -0.583 0.5596
## 0.235 0.8144
## -0.719 0.4723
## 1.066 0.2864
## -0.072 0.9423
## 0.371 0.7108
## 1.248 0.2122
## 0.210 0.8337
## 2.594 0.0095
## 0.321 0.7479
## 1.101 0.2710
## 0.200 0.8413
## 2.178 0.0294
## 0.951 0.3417
## -0.191 0.8486
## 2.155 0.0312
## -1.125 0.2605
## 0.879 0.3794
## 2.434 0.0150More protective in hispanic elem than hispanic middle school women: -0.29, p = 0.048)
Openness
open <- glm(ever_suspended ~ race * sex * open_tipi * school_group,
family = binomial, data = nlsy_l)
summary(open)##
## Call:
## glm(formula = ever_suspended ~ race * sex * open_tipi * school_group,
## family = binomial, data = nlsy_l)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1791 -0.6815 -0.5425 -0.3437 2.6087
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -2.674380 0.350428 -7.632
## racelatinx 3.271668 1.602514 2.042
## raceblack 2.728574 0.442419 6.167
## raceother 0.678791 0.631841 1.074
## sexFEMALE 0.896613 0.608814 1.473
## open_tipi 0.166548 0.061082 2.727
## school_groupmiddle 0.514132 0.475401 1.081
## school_grouphigh 1.170129 0.567028 2.064
## racelatinx:sexFEMALE -14.042931 6.847265 -2.051
## raceblack:sexFEMALE -1.796191 0.748770 -2.399
## raceother:sexFEMALE -1.413309 1.166959 -1.211
## racelatinx:open_tipi -0.468169 0.272276 -1.719
## raceblack:open_tipi -0.216860 0.077729 -2.790
## raceother:open_tipi 0.004162 0.110662 0.038
## sexFEMALE:open_tipi -0.336826 0.108914 -3.093
## racelatinx:school_groupmiddle -2.132854 1.833774 -1.163
## raceblack:school_groupmiddle -1.253633 0.591602 -2.119
## raceother:school_groupmiddle 0.200661 0.855844 0.234
## racelatinx:school_grouphigh -3.375248 2.069562 -1.631
## raceblack:school_grouphigh -1.534598 0.720160 -2.131
## raceother:school_grouphigh -2.005188 1.170378 -1.713
## sexFEMALE:school_groupmiddle -0.342484 0.801588 -0.427
## sexFEMALE:school_grouphigh -0.101543 0.969912 -0.105
## open_tipi:school_groupmiddle -0.043371 0.083187 -0.521
## open_tipi:school_grouphigh -0.110621 0.100032 -1.106
## racelatinx:sexFEMALE:open_tipi 2.168480 1.061726 2.042
## raceblack:sexFEMALE:open_tipi 0.285620 0.134360 2.126
## raceother:sexFEMALE:open_tipi 0.190087 0.207506 0.916
## racelatinx:sexFEMALE:school_groupmiddle 8.613893 7.275858 1.184
## raceblack:sexFEMALE:school_groupmiddle 0.968498 0.981067 0.987
## raceother:sexFEMALE:school_groupmiddle -1.735593 1.696324 -1.023
## racelatinx:sexFEMALE:school_grouphigh 13.651035 7.094615 1.924
## raceblack:sexFEMALE:school_grouphigh 0.092413 1.190638 0.078
## raceother:sexFEMALE:school_grouphigh 2.667729 1.949617 1.368
## racelatinx:open_tipi:school_groupmiddle 0.316782 0.315759 1.003
## raceblack:open_tipi:school_groupmiddle 0.117088 0.104195 1.124
## raceother:open_tipi:school_groupmiddle -0.072910 0.150665 -0.484
## racelatinx:open_tipi:school_grouphigh 0.552884 0.356076 1.553
## raceblack:open_tipi:school_grouphigh 0.150875 0.127413 1.184
## raceother:open_tipi:school_grouphigh 0.289551 0.201706 1.436
## sexFEMALE:open_tipi:school_groupmiddle 0.092294 0.143705 0.642
## sexFEMALE:open_tipi:school_grouphigh 0.037227 0.174661 0.213
## racelatinx:sexFEMALE:open_tipi:school_groupmiddle -1.322460 1.141823 -1.158
## raceblack:sexFEMALE:open_tipi:school_groupmiddle -0.160208 0.176267 -0.909
## raceother:sexFEMALE:open_tipi:school_groupmiddle 0.267258 0.296148 0.902
## racelatinx:sexFEMALE:open_tipi:school_grouphigh -2.065775 1.113710 -1.855
## raceblack:sexFEMALE:open_tipi:school_grouphigh 0.034393 0.214309 0.160
## raceother:sexFEMALE:open_tipi:school_grouphigh -0.455981 0.349408 -1.305
## Pr(>|z|)
## (Intercept) 2.32e-14 ***
## racelatinx 0.04119 *
## raceblack 6.94e-10 ***
## raceother 0.28268
## sexFEMALE 0.14083
## open_tipi 0.00640 **
## school_groupmiddle 0.27949
## school_grouphigh 0.03905 *
## racelatinx:sexFEMALE 0.04028 *
## raceblack:sexFEMALE 0.01645 *
## raceother:sexFEMALE 0.22586
## racelatinx:open_tipi 0.08553 .
## raceblack:open_tipi 0.00527 **
## raceother:open_tipi 0.97000
## sexFEMALE:open_tipi 0.00198 **
## racelatinx:school_groupmiddle 0.24479
## raceblack:school_groupmiddle 0.03409 *
## raceother:school_groupmiddle 0.81463
## racelatinx:school_grouphigh 0.10291
## raceblack:school_grouphigh 0.03310 *
## raceother:school_grouphigh 0.08666 .
## sexFEMALE:school_groupmiddle 0.66919
## sexFEMALE:school_grouphigh 0.91662
## open_tipi:school_groupmiddle 0.60211
## open_tipi:school_grouphigh 0.26879
## racelatinx:sexFEMALE:open_tipi 0.04111 *
## raceblack:sexFEMALE:open_tipi 0.03352 *
## raceother:sexFEMALE:open_tipi 0.35964
## racelatinx:sexFEMALE:school_groupmiddle 0.23645
## raceblack:sexFEMALE:school_groupmiddle 0.32355
## raceother:sexFEMALE:school_groupmiddle 0.30624
## racelatinx:sexFEMALE:school_grouphigh 0.05434 .
## raceblack:sexFEMALE:school_grouphigh 0.93813
## raceother:sexFEMALE:school_grouphigh 0.17121
## racelatinx:open_tipi:school_groupmiddle 0.31574
## raceblack:open_tipi:school_groupmiddle 0.26112
## raceother:open_tipi:school_groupmiddle 0.62844
## racelatinx:open_tipi:school_grouphigh 0.12049
## raceblack:open_tipi:school_grouphigh 0.23636
## raceother:open_tipi:school_grouphigh 0.15114
## sexFEMALE:open_tipi:school_groupmiddle 0.52071
## sexFEMALE:open_tipi:school_grouphigh 0.83122
## racelatinx:sexFEMALE:open_tipi:school_groupmiddle 0.24678
## raceblack:sexFEMALE:open_tipi:school_groupmiddle 0.36340
## raceother:sexFEMALE:open_tipi:school_groupmiddle 0.36682
## racelatinx:sexFEMALE:open_tipi:school_grouphigh 0.06362 .
## raceblack:sexFEMALE:open_tipi:school_grouphigh 0.87250
## raceother:sexFEMALE:open_tipi:school_grouphigh 0.19189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 18409 on 18336 degrees of freedom
## Residual deviance: 16946 on 18289 degrees of freedom
## AIC: 17042
##
## Number of Fisher Scoring iterations: 6plot_model(open, type = "pred", terms = c("open_tipi", "race", "sex", "school_group"))
object1 <- emtrends(open, pairwise ~ race * sex * school_group, var = "open_tipi", adjust = "none")
object1## $emtrends
## race sex school_group open_tipi.trend SE df asymp.LCL asymp.UCL
## white MALE elementary 0.1665 0.0611 Inf 0.0468 0.28627
## latinx MALE elementary -0.3016 0.2653 Inf -0.8217 0.21843
## black MALE elementary -0.0503 0.0481 Inf -0.1445 0.04391
## other MALE elementary 0.1707 0.0923 Inf -0.0102 0.35157
## white FEMALE elementary -0.1703 0.0902 Inf -0.3470 0.00646
## latinx FEMALE elementary 1.5300 1.0223 Inf -0.4735 3.53361
## black FEMALE elementary -0.1015 0.0623 Inf -0.2236 0.02056
## other FEMALE elementary 0.0240 0.1506 Inf -0.2712 0.31915
## white MALE middle 0.1232 0.0565 Inf 0.0125 0.23386
## latinx MALE middle -0.0282 0.1496 Inf -0.3214 0.26500
## black MALE middle 0.0234 0.0403 Inf -0.0556 0.10243
## other MALE middle 0.0544 0.0852 Inf -0.1126 0.22148
## white FEMALE middle -0.1214 0.0748 Inf -0.2680 0.02531
## latinx FEMALE middle 0.5733 0.3812 Inf -0.1739 1.32045
## black FEMALE middle -0.0957 0.0510 Inf -0.1957 0.00428
## other FEMALE middle 0.2672 0.1691 Inf -0.0642 0.59865
## white MALE high 0.0559 0.0792 Inf -0.0993 0.21119
## latinx MALE high 0.1406 0.2154 Inf -0.2815 0.56274
## black MALE high -0.0101 0.0626 Inf -0.1327 0.11260
## other MALE high 0.3496 0.1489 Inf 0.0578 0.64143
## white FEMALE high -0.2437 0.1112 Inf -0.4617 -0.02569
## latinx FEMALE high -0.0563 0.2192 Inf -0.4859 0.37343
## black FEMALE high 0.0104 0.0729 Inf -0.1325 0.15324
## other FEMALE high -0.2159 0.1955 Inf -0.5990 0.16731
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df
## white MALE elementary - latinx MALE elementary 0.468169 0.2723 Inf
## white MALE elementary - black MALE elementary 0.216860 0.0777 Inf
## white MALE elementary - other MALE elementary -0.004162 0.1107 Inf
## white MALE elementary - white FEMALE elementary 0.336826 0.1089 Inf
## white MALE elementary - latinx FEMALE elementary -1.363485 1.0241 Inf
## white MALE elementary - black FEMALE elementary 0.268067 0.0872 Inf
## white MALE elementary - other FEMALE elementary 0.142577 0.1625 Inf
## white MALE elementary - white MALE middle 0.043371 0.0832 Inf
## white MALE elementary - latinx MALE middle 0.194757 0.1616 Inf
## white MALE elementary - black MALE middle 0.143143 0.0732 Inf
## white MALE elementary - other MALE middle 0.112118 0.1049 Inf
## white MALE elementary - white FEMALE middle 0.287903 0.0966 Inf
## white MALE elementary - latinx FEMALE middle -0.406731 0.3861 Inf
## white MALE elementary - black FEMALE middle 0.262264 0.0796 Inf
## white MALE elementary - other FEMALE middle -0.100695 0.1798 Inf
## white MALE elementary - white MALE high 0.110621 0.1000 Inf
## white MALE elementary - latinx MALE high 0.025906 0.2239 Inf
## white MALE elementary - black MALE high 0.176606 0.0875 Inf
## white MALE elementary - other MALE high -0.183092 0.1609 Inf
## white MALE elementary - white FEMALE high 0.410220 0.1269 Inf
## white MALE elementary - latinx FEMALE high 0.222800 0.2276 Inf
## white MALE elementary - black FEMALE high 0.156194 0.0951 Inf
## white MALE elementary - other FEMALE high 0.382401 0.2048 Inf
## latinx MALE elementary - black MALE elementary -0.251308 0.2697 Inf
## latinx MALE elementary - other MALE elementary -0.472331 0.2809 Inf
## latinx MALE elementary - white FEMALE elementary -0.131342 0.2802 Inf
## latinx MALE elementary - latinx FEMALE elementary -1.831654 1.0561 Inf
## latinx MALE elementary - black FEMALE elementary -0.200102 0.2725 Inf
## latinx MALE elementary - other FEMALE elementary -0.325592 0.3051 Inf
## latinx MALE elementary - white MALE middle -0.424798 0.2713 Inf
## latinx MALE elementary - latinx MALE middle -0.273412 0.3046 Inf
## latinx MALE elementary - black MALE middle -0.325026 0.2684 Inf
## latinx MALE elementary - other MALE middle -0.356050 0.2787 Inf
## latinx MALE elementary - white FEMALE middle -0.180265 0.2757 Inf
## latinx MALE elementary - latinx FEMALE middle -0.874899 0.4645 Inf
## latinx MALE elementary - black FEMALE middle -0.205905 0.2702 Inf
## latinx MALE elementary - other FEMALE middle -0.568864 0.3146 Inf
## latinx MALE elementary - white MALE high -0.357548 0.2769 Inf
## latinx MALE elementary - latinx MALE high -0.442263 0.3417 Inf
## latinx MALE elementary - black MALE high -0.291562 0.2726 Inf
## latinx MALE elementary - other MALE high -0.651261 0.3042 Inf
## latinx MALE elementary - white FEMALE high -0.057948 0.2877 Inf
## latinx MALE elementary - latinx FEMALE high -0.245369 0.3442 Inf
## latinx MALE elementary - black FEMALE high -0.311975 0.2752 Inf
## latinx MALE elementary - other FEMALE high -0.085767 0.3296 Inf
## black MALE elementary - other MALE elementary -0.221022 0.1040 Inf
## black MALE elementary - white FEMALE elementary 0.119966 0.1022 Inf
## black MALE elementary - latinx FEMALE elementary -1.580345 1.0234 Inf
## black MALE elementary - black FEMALE elementary 0.051207 0.0787 Inf
## black MALE elementary - other FEMALE elementary -0.074284 0.1581 Inf
## black MALE elementary - white MALE middle -0.173489 0.0742 Inf
## black MALE elementary - latinx MALE middle -0.022103 0.1571 Inf
## black MALE elementary - black MALE middle -0.073717 0.0627 Inf
## black MALE elementary - other MALE middle -0.104742 0.0979 Inf
## black MALE elementary - white FEMALE middle 0.071043 0.0889 Inf
## black MALE elementary - latinx FEMALE middle -0.623591 0.3842 Inf
## black MALE elementary - black FEMALE middle 0.045404 0.0701 Inf
## black MALE elementary - other FEMALE middle -0.317555 0.1758 Inf
## black MALE elementary - white MALE high -0.106239 0.0927 Inf
## black MALE elementary - latinx MALE high -0.190954 0.2207 Inf
## black MALE elementary - black MALE high -0.040254 0.0789 Inf
## black MALE elementary - other MALE high -0.399952 0.1564 Inf
## black MALE elementary - white FEMALE high 0.193360 0.1212 Inf
## black MALE elementary - latinx FEMALE high 0.005940 0.2244 Inf
## black MALE elementary - black FEMALE high -0.060667 0.0873 Inf
## black MALE elementary - other FEMALE high 0.165541 0.2013 Inf
## other MALE elementary - white FEMALE elementary 0.340989 0.1290 Inf
## other MALE elementary - latinx FEMALE elementary -1.359323 1.0264 Inf
## other MALE elementary - black FEMALE elementary 0.272229 0.1113 Inf
## other MALE elementary - other FEMALE elementary 0.146739 0.1766 Inf
## other MALE elementary - white MALE middle 0.047533 0.1082 Inf
## other MALE elementary - latinx MALE middle 0.198919 0.1758 Inf
## other MALE elementary - black MALE middle 0.147305 0.1007 Inf
## other MALE elementary - other MALE middle 0.116280 0.1256 Inf
## other MALE elementary - white FEMALE middle 0.292065 0.1188 Inf
## other MALE elementary - latinx FEMALE middle -0.402568 0.3922 Inf
## other MALE elementary - black FEMALE middle 0.266426 0.1054 Inf
## other MALE elementary - other FEMALE middle -0.096533 0.1926 Inf
## other MALE elementary - white MALE high 0.114783 0.1216 Inf
## other MALE elementary - latinx MALE high 0.030068 0.2343 Inf
## other MALE elementary - black MALE high 0.180769 0.1115 Inf
## other MALE elementary - other MALE high -0.178930 0.1752 Inf
## other MALE elementary - white FEMALE high 0.414383 0.1445 Inf
## other MALE elementary - latinx FEMALE high 0.226962 0.2379 Inf
## other MALE elementary - black FEMALE high 0.160356 0.1176 Inf
## other MALE elementary - other FEMALE high 0.386564 0.2162 Inf
## white FEMALE elementary - latinx FEMALE elementary -1.700312 1.0262 Inf
## white FEMALE elementary - black FEMALE elementary -0.068759 0.1096 Inf
## white FEMALE elementary - other FEMALE elementary -0.194250 0.1755 Inf
## white FEMALE elementary - white MALE middle -0.293456 0.1064 Inf
## white FEMALE elementary - latinx MALE middle -0.142069 0.1747 Inf
## white FEMALE elementary - black MALE middle -0.193684 0.0988 Inf
## white FEMALE elementary - other MALE middle -0.224708 0.1241 Inf
## white FEMALE elementary - white FEMALE middle -0.048923 0.1172 Inf
## white FEMALE elementary - latinx FEMALE middle -0.743557 0.3917 Inf
## white FEMALE elementary - black FEMALE middle -0.074563 0.1036 Inf
## white FEMALE elementary - other FEMALE middle -0.437522 0.1916 Inf
## white FEMALE elementary - white MALE high -0.226205 0.1200 Inf
## white FEMALE elementary - latinx MALE high -0.310920 0.2335 Inf
## white FEMALE elementary - black MALE high -0.160220 0.1098 Inf
## white FEMALE elementary - other MALE high -0.519918 0.1741 Inf
## white FEMALE elementary - white FEMALE high 0.073394 0.1432 Inf
## white FEMALE elementary - latinx FEMALE high -0.114026 0.2371 Inf
## white FEMALE elementary - black FEMALE high -0.180633 0.1160 Inf
## white FEMALE elementary - other FEMALE high 0.045575 0.2153 Inf
## latinx FEMALE elementary - black FEMALE elementary 1.631552 1.0241 Inf
## latinx FEMALE elementary - other FEMALE elementary 1.506062 1.0333 Inf
## latinx FEMALE elementary - white MALE middle 1.406856 1.0238 Inf
## latinx FEMALE elementary - latinx MALE middle 1.558242 1.0331 Inf
## latinx FEMALE elementary - black MALE middle 1.506628 1.0230 Inf
## latinx FEMALE elementary - other MALE middle 1.475604 1.0258 Inf
## latinx FEMALE elementary - white FEMALE middle 1.651388 1.0250 Inf
## latinx FEMALE elementary - latinx FEMALE middle 0.956755 1.0910 Inf
## latinx FEMALE elementary - black FEMALE middle 1.625749 1.0235 Inf
## latinx FEMALE elementary - other FEMALE middle 1.262790 1.0361 Inf
## latinx FEMALE elementary - white MALE high 1.474106 1.0253 Inf
## latinx FEMALE elementary - latinx MALE high 1.389391 1.0447 Inf
## latinx FEMALE elementary - black MALE high 1.540092 1.0242 Inf
## latinx FEMALE elementary - other MALE high 1.180394 1.0330 Inf
## latinx FEMALE elementary - white FEMALE high 1.773706 1.0283 Inf
## latinx FEMALE elementary - latinx FEMALE high 1.586286 1.0455 Inf
## latinx FEMALE elementary - black FEMALE high 1.519679 1.0248 Inf
## latinx FEMALE elementary - other FEMALE high 1.745887 1.0408 Inf
## black FEMALE elementary - other FEMALE elementary -0.125490 0.1630 Inf
## black FEMALE elementary - white MALE middle -0.224696 0.0841 Inf
## black FEMALE elementary - latinx MALE middle -0.073310 0.1620 Inf
## black FEMALE elementary - black MALE middle -0.124924 0.0742 Inf
## black FEMALE elementary - other MALE middle -0.155949 0.1056 Inf
## black FEMALE elementary - white FEMALE middle 0.019836 0.0974 Inf
## black FEMALE elementary - latinx FEMALE middle -0.674798 0.3863 Inf
## black FEMALE elementary - black FEMALE middle -0.005803 0.0805 Inf
## black FEMALE elementary - other FEMALE middle -0.368762 0.1802 Inf
## black FEMALE elementary - white MALE high -0.157446 0.1008 Inf
## black FEMALE elementary - latinx MALE high -0.242161 0.2242 Inf
## black FEMALE elementary - black MALE high -0.091461 0.0883 Inf
## black FEMALE elementary - other MALE high -0.451159 0.1614 Inf
## black FEMALE elementary - white FEMALE high 0.142153 0.1275 Inf
## black FEMALE elementary - latinx FEMALE high -0.045267 0.2279 Inf
## black FEMALE elementary - black FEMALE high -0.111873 0.0959 Inf
## black FEMALE elementary - other FEMALE high 0.114335 0.2052 Inf
## other FEMALE elementary - white MALE middle -0.099206 0.1608 Inf
## other FEMALE elementary - latinx MALE middle 0.052180 0.2123 Inf
## other FEMALE elementary - black MALE middle 0.000566 0.1559 Inf
## other FEMALE elementary - other MALE middle -0.030458 0.1731 Inf
## other FEMALE elementary - white FEMALE middle 0.145326 0.1682 Inf
## other FEMALE elementary - latinx FEMALE middle -0.549307 0.4099 Inf
## other FEMALE elementary - black FEMALE middle 0.119687 0.1590 Inf
## other FEMALE elementary - other FEMALE middle -0.243272 0.2264 Inf
## other FEMALE elementary - white MALE high -0.031956 0.1702 Inf
## other FEMALE elementary - latinx MALE high -0.116671 0.2628 Inf
## other FEMALE elementary - black MALE high 0.034030 0.1631 Inf
## other FEMALE elementary - other MALE high -0.325669 0.2118 Inf
## other FEMALE elementary - white FEMALE high 0.267644 0.1872 Inf
## other FEMALE elementary - latinx FEMALE high 0.080223 0.2660 Inf
## other FEMALE elementary - black FEMALE high 0.013617 0.1673 Inf
## other FEMALE elementary - other FEMALE high 0.239825 0.2468 Inf
## white MALE middle - latinx MALE middle 0.151386 0.1599 Inf
## white MALE middle - black MALE middle 0.099772 0.0694 Inf
## white MALE middle - other MALE middle 0.068747 0.1022 Inf
## white MALE middle - white FEMALE middle 0.244532 0.0937 Inf
## white MALE middle - latinx FEMALE middle -0.450101 0.3854 Inf
## white MALE middle - black FEMALE middle 0.218893 0.0761 Inf
## white MALE middle - other FEMALE middle -0.144066 0.1783 Inf
## white MALE middle - white MALE high 0.067250 0.0973 Inf
## white MALE middle - latinx MALE high -0.017465 0.2226 Inf
## white MALE middle - black MALE high 0.133236 0.0843 Inf
## white MALE middle - other MALE high -0.226463 0.1592 Inf
## white MALE middle - white FEMALE high 0.366850 0.1247 Inf
## white MALE middle - latinx FEMALE high 0.179429 0.2264 Inf
## white MALE middle - black FEMALE high 0.112823 0.0922 Inf
## white MALE middle - other FEMALE high 0.339031 0.2035 Inf
## latinx MALE middle - black MALE middle -0.051614 0.1549 Inf
## latinx MALE middle - other MALE middle -0.082639 0.1722 Inf
## latinx MALE middle - white FEMALE middle 0.093146 0.1673 Inf
## latinx MALE middle - latinx FEMALE middle -0.601488 0.4095 Inf
## latinx MALE middle - black FEMALE middle 0.067507 0.1581 Inf
## latinx MALE middle - other FEMALE middle -0.295452 0.2258 Inf
## latinx MALE middle - white MALE high -0.084136 0.1693 Inf
## latinx MALE middle - latinx MALE high -0.168851 0.2622 Inf
## latinx MALE middle - black MALE high -0.018150 0.1622 Inf
## latinx MALE middle - other MALE high -0.377849 0.2111 Inf
## latinx MALE middle - white FEMALE high 0.215464 0.1864 Inf
## latinx MALE middle - latinx FEMALE high 0.028043 0.2654 Inf
## latinx MALE middle - black FEMALE high -0.038564 0.1664 Inf
## latinx MALE middle - other FEMALE high 0.187644 0.2462 Inf
## black MALE middle - other MALE middle -0.031025 0.0943 Inf
## black MALE middle - white FEMALE middle 0.144760 0.0850 Inf
## black MALE middle - latinx FEMALE middle -0.549873 0.3833 Inf
## black MALE middle - black FEMALE middle 0.119121 0.0650 Inf
## black MALE middle - other FEMALE middle -0.243838 0.1738 Inf
## black MALE middle - white MALE high -0.032522 0.0889 Inf
## black MALE middle - latinx MALE high -0.117237 0.2191 Inf
## black MALE middle - black MALE high 0.033464 0.0744 Inf
## black MALE middle - other MALE high -0.326235 0.1542 Inf
## black MALE middle - white FEMALE high 0.267078 0.1183 Inf
## black MALE middle - latinx FEMALE high 0.079657 0.2229 Inf
## black MALE middle - black FEMALE high 0.013051 0.0833 Inf
## black MALE middle - other FEMALE high 0.239259 0.1996 Inf
## other MALE middle - white FEMALE middle 0.175785 0.1134 Inf
## other MALE middle - latinx FEMALE middle -0.518849 0.3906 Inf
## other MALE middle - black FEMALE middle 0.150146 0.0993 Inf
## other MALE middle - other FEMALE middle -0.212813 0.1894 Inf
## other MALE middle - white MALE high -0.001497 0.1164 Inf
## other MALE middle - latinx MALE high -0.086212 0.2316 Inf
## other MALE middle - black MALE high 0.064488 0.1057 Inf
## other MALE middle - other MALE high -0.295210 0.1715 Inf
## other MALE middle - white FEMALE high 0.298102 0.1401 Inf
## other MALE middle - latinx FEMALE high 0.110682 0.2352 Inf
## other MALE middle - black FEMALE high 0.044075 0.1122 Inf
## other MALE middle - other FEMALE high 0.270283 0.2133 Inf
## white FEMALE middle - latinx FEMALE middle -0.694634 0.3885 Inf
## white FEMALE middle - black FEMALE middle -0.025639 0.0906 Inf
## white FEMALE middle - other FEMALE middle -0.388598 0.1849 Inf
## white FEMALE middle - white MALE high -0.177282 0.1090 Inf
## white FEMALE middle - latinx MALE high -0.261997 0.2280 Inf
## white FEMALE middle - black MALE high -0.111297 0.0976 Inf
## white FEMALE middle - other MALE high -0.470995 0.1666 Inf
## white FEMALE middle - white FEMALE high 0.122317 0.1340 Inf
## white FEMALE middle - latinx FEMALE high -0.065103 0.2317 Inf
## white FEMALE middle - black FEMALE high -0.131710 0.1045 Inf
## white FEMALE middle - other FEMALE high 0.094498 0.2093 Inf
## latinx FEMALE middle - black FEMALE middle 0.668994 0.3846 Inf
## latinx FEMALE middle - other FEMALE middle 0.306036 0.4170 Inf
## latinx FEMALE middle - white MALE high 0.517352 0.3894 Inf
## latinx FEMALE middle - latinx MALE high 0.432637 0.4378 Inf
## latinx FEMALE middle - black MALE high 0.583337 0.3863 Inf
## latinx FEMALE middle - other MALE high 0.223639 0.4093 Inf
## latinx FEMALE middle - white FEMALE high 0.816951 0.3971 Inf
## latinx FEMALE middle - latinx FEMALE high 0.629531 0.4398 Inf
## latinx FEMALE middle - black FEMALE high 0.562924 0.3881 Inf
## latinx FEMALE middle - other FEMALE high 0.789132 0.4284 Inf
## black FEMALE middle - other FEMALE middle -0.362959 0.1766 Inf
## black FEMALE middle - white MALE high -0.151643 0.0942 Inf
## black FEMALE middle - latinx MALE high -0.236358 0.2213 Inf
## black FEMALE middle - black MALE high -0.085657 0.0807 Inf
## black FEMALE middle - other MALE high -0.445356 0.1574 Inf
## black FEMALE middle - white FEMALE high 0.147957 0.1224 Inf
## black FEMALE middle - latinx FEMALE high -0.039464 0.2251 Inf
## black FEMALE middle - black FEMALE high -0.106070 0.0890 Inf
## black FEMALE middle - other FEMALE high 0.120138 0.2020 Inf
## other FEMALE middle - white MALE high 0.211316 0.1867 Inf
## other FEMALE middle - latinx MALE high 0.126601 0.2738 Inf
## other FEMALE middle - black MALE high 0.277302 0.1803 Inf
## other FEMALE middle - other MALE high -0.082397 0.2253 Inf
## other FEMALE middle - white FEMALE high 0.510915 0.2024 Inf
## other FEMALE middle - latinx FEMALE high 0.323495 0.2769 Inf
## other FEMALE middle - black FEMALE high 0.256889 0.1841 Inf
## other FEMALE middle - other FEMALE high 0.483096 0.2585 Inf
## white MALE high - latinx MALE high -0.084715 0.2295 Inf
## white MALE high - black MALE high 0.065986 0.1010 Inf
## white MALE high - other MALE high -0.293713 0.1686 Inf
## white MALE high - white FEMALE high 0.299600 0.1365 Inf
## white MALE high - latinx FEMALE high 0.112179 0.2331 Inf
## white MALE high - black FEMALE high 0.045573 0.1077 Inf
## white MALE high - other FEMALE high 0.271780 0.2109 Inf
## latinx MALE high - black MALE high 0.150700 0.2243 Inf
## latinx MALE high - other MALE high -0.208998 0.2618 Inf
## latinx MALE high - white FEMALE high 0.384315 0.2424 Inf
## latinx MALE high - latinx FEMALE high 0.196894 0.3073 Inf
## latinx MALE high - black FEMALE high 0.130288 0.2274 Inf
## latinx MALE high - other FEMALE high 0.356495 0.2909 Inf
## black MALE high - other MALE high -0.359698 0.1615 Inf
## black MALE high - white FEMALE high 0.233614 0.1276 Inf
## black MALE high - latinx FEMALE high 0.046193 0.2280 Inf
## black MALE high - black FEMALE high -0.020413 0.0961 Inf
## black MALE high - other FEMALE high 0.205795 0.2053 Inf
## other MALE high - white FEMALE high 0.593312 0.1858 Inf
## other MALE high - latinx FEMALE high 0.405892 0.2650 Inf
## other MALE high - black FEMALE high 0.339285 0.1658 Inf
## other MALE high - other FEMALE high 0.565493 0.2457 Inf
## white FEMALE high - latinx FEMALE high -0.187420 0.2458 Inf
## white FEMALE high - black FEMALE high -0.254027 0.1330 Inf
## white FEMALE high - other FEMALE high -0.027819 0.2249 Inf
## latinx FEMALE high - black FEMALE high -0.066606 0.2310 Inf
## latinx FEMALE high - other FEMALE high 0.159601 0.2937 Inf
## black FEMALE high - other FEMALE high 0.226208 0.2086 Inf
## z.ratio p.value
## 1.719 0.0855
## 2.790 0.0053
## -0.038 0.9700
## 3.093 0.0020
## -1.331 0.1830
## 3.073 0.0021
## 0.877 0.3803
## 0.521 0.6021
## 1.205 0.2281
## 1.956 0.0505
## 1.069 0.2850
## 2.980 0.0029
## -1.053 0.2921
## 3.295 0.0010
## -0.560 0.5754
## 1.106 0.2688
## 0.116 0.9079
## 2.019 0.0434
## -1.138 0.2552
## 3.233 0.0012
## 0.979 0.3276
## 1.642 0.1005
## 1.867 0.0619
## -0.932 0.3514
## -1.681 0.0927
## -0.469 0.6393
## -1.734 0.0829
## -0.734 0.4628
## -1.067 0.2859
## -1.566 0.1174
## -0.898 0.3694
## -1.211 0.2259
## -1.278 0.2014
## -0.654 0.5132
## -1.884 0.0596
## -0.762 0.4460
## -1.808 0.0706
## -1.291 0.1966
## -1.294 0.1956
## -1.069 0.2848
## -2.141 0.0323
## -0.201 0.8404
## -0.713 0.4759
## -1.134 0.2569
## -0.260 0.7947
## -2.124 0.0337
## 1.174 0.2404
## -1.544 0.1225
## 0.651 0.5152
## -0.470 0.6384
## -2.339 0.0193
## -0.141 0.8881
## -1.175 0.2400
## -1.070 0.2845
## 0.799 0.4244
## -1.623 0.1046
## 0.648 0.5172
## -1.806 0.0708
## -1.147 0.2516
## -0.865 0.3868
## -0.510 0.6100
## -2.557 0.0106
## 1.596 0.1105
## 0.026 0.9789
## -0.695 0.4872
## 0.822 0.4109
## 2.643 0.0082
## -1.324 0.1854
## 2.445 0.0145
## 0.831 0.4061
## 0.439 0.6604
## 1.132 0.2578
## 1.463 0.1435
## 0.926 0.3546
## 2.458 0.0140
## -1.026 0.3047
## 2.527 0.0115
## -0.501 0.6163
## 0.944 0.3453
## 0.128 0.8979
## 1.621 0.1050
## -1.022 0.3070
## 2.867 0.0041
## 0.954 0.3400
## 1.364 0.1727
## 1.788 0.0738
## -1.657 0.0975
## -0.627 0.5304
## -1.107 0.2685
## -2.758 0.0058
## -0.813 0.4160
## -1.961 0.0499
## -1.811 0.0701
## -0.418 0.6763
## -1.898 0.0577
## -0.720 0.4717
## -2.283 0.0224
## -1.885 0.0595
## -1.332 0.1830
## -1.460 0.1444
## -2.987 0.0028
## 0.513 0.6082
## -0.481 0.6305
## -1.558 0.1193
## 0.212 0.8323
## 1.593 0.1111
## 1.458 0.1450
## 1.374 0.1694
## 1.508 0.1315
## 1.473 0.1408
## 1.438 0.1503
## 1.611 0.1072
## 0.877 0.3805
## 1.588 0.1122
## 1.219 0.2229
## 1.438 0.1505
## 1.330 0.1835
## 1.504 0.1326
## 1.143 0.2532
## 1.725 0.0845
## 1.517 0.1292
## 1.483 0.1381
## 1.677 0.0934
## -0.770 0.4413
## -2.673 0.0075
## -0.452 0.6510
## -1.684 0.0922
## -1.477 0.1396
## 0.204 0.8386
## -1.747 0.0806
## -0.072 0.9425
## -2.046 0.0407
## -1.562 0.1182
## -1.080 0.2801
## -1.036 0.3003
## -2.796 0.0052
## 1.115 0.2648
## -0.199 0.8426
## -1.167 0.2433
## 0.557 0.5774
## -0.617 0.5374
## 0.246 0.8058
## 0.004 0.9971
## -0.176 0.8603
## 0.864 0.3875
## -1.340 0.1802
## 0.753 0.4516
## -1.074 0.2827
## -0.188 0.8510
## -0.444 0.6571
## 0.209 0.8347
## -1.538 0.1241
## 1.430 0.1528
## 0.302 0.7629
## 0.081 0.9351
## 0.972 0.3311
## 0.947 0.3438
## 1.438 0.1505
## 0.672 0.5013
## 2.608 0.0091
## -1.168 0.2428
## 2.876 0.0040
## -0.808 0.4190
## 0.691 0.4894
## -0.078 0.9375
## 1.581 0.1140
## -1.422 0.1549
## 2.941 0.0033
## 0.793 0.4280
## 1.223 0.2211
## 1.666 0.0957
## -0.333 0.7390
## -0.480 0.6313
## 0.557 0.5776
## -1.469 0.1419
## 0.427 0.6693
## -1.309 0.1907
## -0.497 0.6192
## -0.644 0.5196
## -0.112 0.9109
## -1.790 0.0734
## 1.156 0.2477
## 0.106 0.9159
## -0.232 0.8168
## 0.762 0.4459
## -0.329 0.7421
## 1.703 0.0886
## -1.434 0.1515
## 1.832 0.0670
## -1.403 0.1607
## -0.366 0.7145
## -0.535 0.5926
## 0.449 0.6531
## -2.115 0.0344
## 2.258 0.0240
## 0.357 0.7208
## 0.157 0.8755
## 1.199 0.2307
## 1.550 0.1212
## -1.328 0.1841
## 1.511 0.1307
## -1.124 0.2611
## -0.013 0.9897
## -0.372 0.7097
## 0.610 0.5420
## -1.721 0.0853
## 2.127 0.0334
## 0.471 0.6380
## 0.393 0.6943
## 1.267 0.2050
## -1.788 0.0738
## -0.283 0.7771
## -2.102 0.0356
## -1.627 0.1038
## -1.149 0.2505
## -1.141 0.2539
## -2.827 0.0047
## 0.912 0.3615
## -0.281 0.7787
## -1.261 0.2074
## 0.451 0.6517
## 1.739 0.0820
## 0.734 0.4630
## 1.329 0.1839
## 0.988 0.3231
## 1.510 0.1310
## 0.546 0.5848
## 2.057 0.0397
## 1.432 0.1523
## 1.450 0.1470
## 1.842 0.0655
## -2.055 0.0399
## -1.609 0.1075
## -1.068 0.2855
## -1.061 0.2888
## -2.830 0.0047
## 1.209 0.2266
## -0.175 0.8608
## -1.192 0.2332
## 0.595 0.5521
## 1.132 0.2578
## 0.462 0.6438
## 1.538 0.1240
## -0.366 0.7146
## 2.524 0.0116
## 1.168 0.2426
## 1.395 0.1630
## 1.869 0.0616
## -0.369 0.7120
## 0.654 0.5134
## -1.742 0.0816
## 2.194 0.0282
## 0.481 0.6303
## 0.423 0.6721
## 1.288 0.1976
## 0.672 0.5016
## -0.798 0.4247
## 1.586 0.1128
## 0.641 0.5217
## 0.573 0.5666
## 1.226 0.2203
## -2.227 0.0259
## 1.831 0.0672
## 0.203 0.8394
## -0.212 0.8317
## 1.003 0.3161
## 3.193 0.0014
## 1.532 0.1256
## 2.047 0.0407
## 2.301 0.0214
## -0.762 0.4458
## -1.910 0.0561
## -0.124 0.9016
## -0.288 0.7731
## 0.543 0.5869
## 1.084 0.2783